Number of terminal estimation device and number of terminal estimation method

ABSTRACT

A number-of-terminals estimation device has a unit to acquire location data; a unit to acquire location acquisition time information of second location data immediately preceding the first location data and third location data immediately following the first location data, from location data including the same identification information; a unit to calculate a feature amount of the first location data, based on at least two of the location acquisition time information of the first to third location data; a unit to acquire observation target location data including location acquisition time information after an observation start time and before an observation end time and including location information corresponding to observation area information; and a unit to estimate the number of terminals located in the observation area during the observation period, based on feature amounts of the observation target location data and the length of the observation period.

TECHNICAL FIELD

The present invention relates to a number-of-terminals estimation deviceand a number-of-terminals estimation method to estimate the number ofterminals located in a certain area, using location information aboutmobile terminals obtained from network facilities of the mobileterminals (e.g., cell phones).

BACKGROUND ART

In the network facilities of cell-phone operators, there appearoperational data such as location data of cell phones and attribute dataof users to provide telecommunications services to users of cell phones.By performing statistical processing such as totalization on theseoperational data, we can obtain estimated values about demographics suchas “population distribution,” “population change,” and “populationcomposition.” Of these, the “population distribution” is a populationdistributed in each of areas, the “population change” is a change ofpopulation along a time axis in a certain area, and the “populationcomposition” is information about a population distribution or apopulation change, for example, in divisions such as genders or agegroups.

The aforementioned location data is, for example, location registrationsignals. They are signals transmitted approximately at regular intervalsfrom a cell phone to a serving base station, and when a certain basestation receives a location registration signal of a certain cell phone,it can be estimated that the cell phone exists in a sector being acoverage area of the base station, at a time of reception.

Another example of the location data is GPS information. This isinformation about the GPS positioning result transmitted at regularintervals from a cell phone to a serving base station or transmitted inaccordance with an operation of the terminal or in accordance with arequest from a cell phone network. With this information, it can also beestimated similarly that the cell phone exists around a locationindicated by the GPS positioning result, at a time of reception of theGPS information (e.g., cf. Patent Literature 1).

If the number of cell phones (the number of terminals) existing in acertain geographical area can be estimated from the observation resultof the location data as described above, we can expect that estimatedvalues about the aforementioned various demographics are obtained byfurther taking a subscription rate of cell phones or the like intoconsideration.

CITATION LIST Patent Literature

-   Patent Literature 1: Japanese Patent Application Laid-open No.    2003-44969

SUMMARY OF INVENTION Technical Problem

However, it is not easy to correctly estimate the number of terminalsfrom the location data as described above. This is because the locationdata of terminals such as the location registration signals and the GPSinformation are not always transmitted to the cell phone network buttransmitted with some temporal intervals and the temporal intervals oftransmission are not constant.

For example, let us consider a case where the number of terminals isestimated using the location registration signals as location data. Ifwe can assume that the location registration signals are transmittedperfectly at regular intervals, the number of location registrationsignals received in a prescribed observation period in a certain sectorwill be proportional to the number of terminals in the sector. In fact,however, the location registration signals are basically transmitted,for example, on a periodic basis by a timer in each cell phone, butthere are cases where a location registration signal is transmittedregardless of a state of the timer, at a time of a crossing betweensectors of a certain specific base station and where transmission isdelayed because of influence of calls, an out-of-service area, or thelike. Concerning the GPS information, cycles of transmission andreception are not constant, either, because of various effects such asan out-of-service area and an operation of the terminal.

Let us consider combinational use of the location registration signalsand the GPS information as location data. Since in this case the volumeof information available for the number-of-terminals estimationincreases, we can expect that the number of terminals can be estimatedwith higher accuracy. In this case, however, the frequency oftransmission and reception of the location data including both of thetwo pieces of information becomes more random than in the cases usingeach of the two pieces of information, and if the number of terminals isestimated on the assumption that the cycles of transmission andreception are constant, the estimation accuracy could degrade on thecontrary.

For this reason, it is necessary to take account of the variation inreception intervals of received signals, instead of simply counting thenumber of received signals, in order to accurately estimate the numberof terminals.

The present invention has been accomplished in view of the foregoing andit is an object of the invention to accurately estimate the number ofterminals while correcting the influence of variation in receptionintervals, in estimating the number of terminals through the use of thelocation data.

Solution to Problem

A number-of-terminals estimation device according to an aspect of thepresent invention is a number-of-terminals estimation device comprising:location data acquisition means for acquiring location data includingidentification information to identify a mobile terminal, locationinformation about a location of the mobile terminal, and locationacquisition time information on a time when the location information isacquired; preceding and following location data acquisition means for,concerning a piece of first location data, acquiring locationacquisition time information of second location data which is locationdata immediately preceding the first location data, and locationacquisition time information of third location data which is locationdata immediately following the first location data, from location dataincluding the same identification information as that of the firstlocation data; feature amount calculation means for calculating afeature amount of the first location data, based on at least two of thelocation acquisition time information of the first location data, thelocation acquisition time information of the second location data, andthe location acquisition time information of the third location data;observation target acquisition means for acquiring as observation targetlocation data, one or more pieces of location data including locationacquisition time information after an observation start time and beforean observation end time about an observation period to be observed, andincluding location information associated with observation areainformation about an observation area to be observed; andnumber-of-terminals estimation means for estimating the number ofterminals located in the observation area during the observation period,based on a feature amount of the observation target location data, and alength of the observation period which is a difference between theobservation start time and the observation end time. The “featureamount” is information corresponding to an estimated generation densityon the location data generated by the mobile terminal and the “estimatedgeneration density” herein means an estimated value of the number oflocation data which the mobile terminal having generated the locationdata generates per unit time around a time of generation of the locationdata (corresponding to the foregoing location acquisition time). Theforegoing number-of-terminals estimation device may be configured asfollows: the feature amount calculation means calculates a differencebetween a location acquisition time of the second location data and alocation acquisition time of the third location data, as the featureamount of the first location data, and the number-of-terminalsestimation means estimates the number of terminals to be a numeralobtained by dividing a sum of feature amounts of the observation targetlocation data by twice the length of the observation period. Thedetailed principle will be described later, but the number of terminalscan be accurately estimated while correcting the influence of variationin reception intervals, by the configuration wherein the feature amountcalculation means calculates the difference between the locationacquisition times of the second and third location data as the featureamount of the first location data and wherein the number-of-terminalsestimation means estimates the number of terminals to be the numeralobtained by dividing the sum of the feature amounts of the observationtarget location data by twice the length of the observation period.

The feature amount calculation means may operate as follows: when adifference between a location acquisition time of the first locationdata and the location acquisition time of the second location data islarger than a predetermined value, the feature amount calculation meanscalculates the feature amount of the first location information, usingas the location acquisition time of the second location data, a time setbackward by a predetermined time from the location acquisition time ofthe first location data. Similarly, the feature amount calculation meansmay operate as follows: when a difference between the locationacquisition time of the first location data and the location acquisitiontime of the third location data is larger than a predetermined value,the feature amount calculation means calculates the feature amount ofthe first location information, using as the location acquisition timeof the third location data, a time set forward by a predetermined timefrom the location acquisition time of the first location data. As thefeature amount calculation means is made to operate as described above,when an acquisition time interval of location data becomes abnormallylong because of the mobile terminal being located in an out-of-servicearea or because the mobile terminal being in a power-off mode, it isfeasible to prevent the abnormally long acquisition time interval fromexcessively affecting the calculation result.

Incidentally, the feature amount calculation means may operate asfollows: the feature amount calculation means makes a determination onwhether or not the first location data includes location registrationinformation generated due to a crossing across a location registrationarea border, and a determination on whether or not the third locationdata includes location registration information generated due to acrossing across a location registration area border; the feature amountcalculation means calculates the feature amount of the first locationdata, using at least two of the location acquisition time information ofthe first location data, the location acquisition time information ofthe second location data, and the location acquisition time informationof the third location data, according to the result of the determinationon whether or not the first location data includes location registrationinformation generated due to a crossing across a location registrationarea border and the result of the determination on whether or not thethird location data includes location registration information generateddue to a crossing across a location registration area border. In thiscase, though the detailed principle will be described later, the featureamount with high accuracy can be obtained taking account of thecharacteristics of the generation timing about the location registrationinformation generated due to a crossing across a location registrationarea border. The “location registration information generated due to acrossing across a location registration area border” means locationregistration information generated because of a crossing of the mobileterminal across a border of a location registration area.

More specifically, the feature amount calculation means may operate asfollows: when the first location data includes location registrationinformation generated due to a crossing across a location registrationarea border, the feature amount calculation means sets the locationacquisition time of the first location data to a first variable; whenthe first location data does not include location registrationinformation generated due to a crossing across a location registrationarea border, the feature amount calculation means sets a midpoint timebetween the location acquisition time of the first location data and thelocation acquisition time of the second location data to the firstvariable; when the third location data includes location registrationinformation generated due to a crossing across a location registrationarea border, the feature amount calculation means sets the locationacquisition time of the third location data to a second variable; whenthe third location data does not include location registrationinformation generated due to a crossing across a location registrationarea border, the feature amount calculation means sets a midpoint timebetween the location acquisition time of the first location data and thelocation acquisition time of the third location data to the secondvariable; the feature amount calculation means calculates the featureamount of the first location data, based on a difference between the setfirst variable and second variable.

The feature amount calculation means may operate as follows: when adifference between the location acquisition time of the first locationdata and a first variable is larger than a predetermined value, thefeature amount calculation means calculates the feature amount of thefirst location data, using as the first variable, a time set backward bya predetermined time from the location acquisition time of the firstlocation data. Similarly, the feature amount calculation means mayoperate as follows: when a difference between the location acquisitiontime of the first location data and a second variable is larger than apredetermined value, the feature amount calculation means calculates thefeature amount of the first location data, using as the second variable,a time set forward by a predetermined time from the location acquisitiontime of the first location data. As the feature amount calculation meansis made to operate as described above, when an acquisition time intervalof location data becomes abnormally long because of the mobile terminalbeing located in an out-of-service area or because of the mobileterminal being in a power-off mode, it is feasible to prevent theabnormally long acquisition time interval from excessively affecting thecalculation result.

Here, the number-of-terminals estimation device may be configured sothat targets of calculation of the feature amount are the observationtarget location data acquired by the observation target acquisitionmeans or so that the targets are all pieces of the location dataacquired by the location data acquisition means. Of these, when thetargets are the observation target location data acquired by theobservation target acquisition means, the preceding and followinglocation data acquisition means defines each piece of the observationtarget location data acquired by the observation target acquisitionmeans, as the first location data, and acquires, for the first locationdata, the location acquisition time information of the second locationdata and the location acquisition time information of the third locationdata, the feature amount calculation means calculates the feature amountof each piece of the observation target location data, and thenumber-of-terminals estimation means estimates the number of terminals,using feature amounts of respective pieces of the observation targetlocation data obtained by calculation.

On the other hand, when the targets are all pieces of the location dataacquired by the location data acquisition means, the preceding andfollowing location data acquisition means defines each piece of all thelocation data acquired by the location data acquisition means, as thefirst location data, and acquires, for the first location data, thelocation acquisition time information of the second location data andthe location acquisition time information of the third location data,the feature amount calculation means calculates the feature amount ofeach piece of all the location data, and the number-of-terminalsestimation means estimates the number of terminals, using featureamounts of the observation target location data among feature amounts ofrespective pieces of all the location data obtained by calculation.

A number-of-terminals estimation device according to another aspect ofthe present invention may be configured to comprise: location dataacquisition means for acquiring location data including identificationinformation to identify a mobile terminal, location information about alocation of the mobile terminal, and location acquisition timeinformation on a time when the location information is acquired;preceding and following location data acquisition means for, concerninga piece of first location data, acquiring location acquisition timeinformation of second location data which is location data immediatelypreceding the first location data, and location acquisition timeinformation of third location data which is location data immediatelyfollowing the first location data, from location data including the sameidentification information as that of the first location data; featureamount calculation means for calculating a feature amount of the firstlocation data, based on at least the location acquisition timeinformation of the second location data and the location acquisitiontime information of the third location data; observation targetacquisition means for acquiring as observation target location data, oneor more pieces of location data including location acquisition timeinformation after an observation start time and before an observationend time about an observation period to be observed, and includinglocation information associated with observation area information aboutan observation area to be observed; and number-of-terminals estimationmeans for estimating the number of terminals located in the observationarea during the observation period, based on a feature amount of theobservation target location data, and a length of the observation periodwhich is a difference between the observation start time and theobservation end time.

The number-of-terminals estimation device may be configured as follows:it further comprises scaling factor storage means for storing an scalingfactor for conversion of the number of terminals into a population; thenumber-of-terminals estimation means estimates at least one of apopulation in the observation area during the observation period, andpopulations in respective population estimation units which are units ofestimation for population, based on feature amounts of the observationtarget location data, the length of the observation period, and thescaling factor. The foregoing “population estimation units” can be, forexample, attributes, places, time zones, and so on. The scaling factorto be used may be one stored in the scaling factor storage means or onederived as follows. The scaling factor can be, for example, a reciprocalof “a product of a presence rate and a terminal penetration rate (i.e.,a ratio of a presence count to a population).” The “presence rate”herein means a ratio of a presence count to the number of subscriptionsand the “penetration rate” a ratio of the number of subscriptions to apopulation. Such an scaling factor is preferably derived for each of theforegoing population estimation units, but it is not essential.

The scaling factor may be derived, for example, using the number ofterminals (presence count) estimated based on the feature amounts andthe length of the observation period as follows. Namely, the featureamounts are calculated from the location data, the numbers of terminalsin respective scaling factor calculation units are totalized based onthe feature amounts and the observation period length to obtain usercount pyramid data, and population pyramid data in the same scalingfactor calculation units preliminarily obtained as statistical data(e.g., the Basic Resident Register or the like) is acquired. Then anacquisition rate of location data (i.e., presence count/population) iscalculated in each of the scaling factor calculation units with the usercount pyramid data and the population pyramid data. The “acquisitionrate of location data (i.e., presence count/population)” obtained hereincorresponds to the aforementioned “product of a presence rate and aterminal penetration rate.” A reciprocal of the “acquisition rate oflocation data” obtained in this manner can be derived as an scalingfactor. The scaling factor calculation units for calculation of thescaling factor to be employed may be, for example, prefectures ofaddresses, age groups at 5-year or 10-year intervals, genders, timezones of one-hour intervals, and so on, or may be combinations of two ormore of these. For example, when an scaling factor calculation unit is“men in their twenties residing in Tokyo,” location data extracted islocation data corresponding to men in their twenties residing in Tokyo(namely, the address information in user attributes of which is Tokyo)in the whole of Japan; the number of terminals is counted to obtain usercount pyramid data; population pyramid data about men in their twentiesresiding in Tokyo is acquired from the statistical data. In obtainingthe user count pyramid data, as to the condition of “residing in Tokyo,”the device does not extract only the location data of users residing inTokyo, but the device extracts the location data the address informationin user attributes of which is Tokyo. Then the acquisition rate (i.e.,presence count/population) of the location data in the scaling factorcalculation unit (men in their twenties residing in Tokyo herein) iscalculated from the user count pyramid data and the population pyramiddata, and a reciprocal of the obtained “acquisition rate of locationdata” can be derived as an scaling factor. In the present specificationthe description is given on the assumption that the scaling factorcalculation units are equal to the population estimation units, but itis just an example, without having to be limited to this example.

The number-of-terminals estimation device may further comprise:conversion means for converting estimated values in respectiveobservation areas obtained by estimation by the number-of-terminalsestimation means, into estimated values in respective output unitsdifferent from the observation areas, based on area ratios of overlapregions between the output units and the observation areas to theobservation areas. The conversion means may operate as follows: whenthere are at least two communication areas out of a communication areaof an indoor station and communications areas of a plurality of outdoorstations using respective frequency bands with different coverage areas,overlapping in a geographically identical observation area, theconversion means performs conversion into the estimated values in therespective output units based on the area ratios for each of theoverlapping communication regions and addition of the estimated valuesafter the conversion for each of the communication regions, therebyobtaining the estimated values in the respective output units.

The number-of-terminals estimation means may estimate populationsseparately in respective output units and in respective populationestimation units, based on feature amounts of the observation targetlocation data, the length of the observation period, an scaling factorfor conversion of the number of terminals into a population, and arearatios of overlap regions between observation areas and output unitsdifferent from the observation areas to the observation areas.

A mode of estimating populations separately in respective output unitsand in respective population estimation units may be as follows: priorto the estimation of populations by the number-of-terminals estimationmeans, location data is associated with the feature amount, the scalingfactor, and a combination of the area ratio and an output unit IDrelated to the area ratio; the number-of-terminals estimation meanscalculates (feature amount×area ratio×scaling factor) on location datawith which the same output unit ID is associated, totalizes values of(feature amount×area ratio×scaling factor) in respective output unitsobtained, for each of the population estimation units, and estimates thepopulations in respective output units and in respective populationestimation units, based on total values in respective output units andin respective population estimation units obtained and the length of theobservation period.

The number-of-terminals estimation device may further comprise:observation period acquisition means for acquiring observation periodinformation including a set of an observation start time and anobservation end time; and observation area acquisition means foracquiring observation area information associated with one or morepieces of location information.

The number-of-terminals estimation device may further comprise outputmeans for outputting the estimated value obtained. An output form by theoutput means is allowed to be at least one of a drawing showing apopulation distribution, a drawing showing a time-series populationchange, and a drawing showing a population composition, and an outputunit by the output means is allowed to be set according to at least oneof an attribute of a user of a mobile terminal, a time zone, and aplace.

The number-of-terminals estimation device may be configured as follows:it further comprises unidentifiability securing means for performing anunidentifiability securing process including a conversion intoirreversible code by a one-way function on identification informationincluded in the location data acquired by the location data acquisitionmeans; the unidentifiability securing means operates as follows: when aprocess using attribute information of a user of a mobile terminal iscarried out, the unidentifiability securing means performs theunidentifiability securing process on the attribute information, beforethe process.

The number-of-terminals estimation device may further comprise:concealment process means for, before an estimated value obtained isoutput, performing a concealment process on the estimated value on thebasis of a predetermined reference. In that case, the concealmentprocess means may operate as follows: the concealment process meansdetermines whether or not the number of source terminals indicative offrom how many terminals the location data in each area as foundation ofestimation was acquired, is less than a reference value for adetermination that the concealment process is needed; when the number ofsource terminals of the location data in a certain area is less than thereference value, the concealment process means conceals the estimatedvalue about the area. The number of source terminals indicates theunique number of terminals without redundancy of identical terminal.

The concealment method adopted herein can be, for example, a method ofsetting the estimated value to zero, a method of expressing theestimated value by a predetermined letter or mark (e.g., “X” or thelike), and so on. On the other hand, when the number of source terminalsof the location data in an area is not less than the reference value,the concealment process means may be configured not to perform theconcealment process on the estimated value or may be configured toperform rounding as described below. Namely, the concealment processmeans may operate as follows: the concealment process means rounds theestimated value on the area, based on an upper limit value and a lowerlimit value of a class to which the estimated value on the area belongsout of a plurality of classes used in output of estimated value, a classinterval, and the estimated value, to the upper limit value and thelower limit value with respective probability values according to adifference from the upper limit value and a difference from the lowerlimit value.

The invention of the number-of-terminals estimation devices describedabove can also be regarded as the invention of number-of-terminalsestimation methods executed by the number-of-terminals estimationdevices, with the same action and effect. Specifically, the methods canbe described as below, according to the configuration wherein thefeature amount is calculated for each target of the observation targetlocation data or according to the configuration wherein the featureamount is calculated for each target of all the location data acquired.

A number-of-terminals estimation method according to an aspect of thepresent invention is a number-of-terminals estimation method executed bya number-of-terminals estimation device, comprising: a location dataacquisition step of acquiring location data including identificationinformation to identify a mobile terminal, location information about alocation of the mobile terminal, and location acquisition timeinformation on a time when the location information is acquired; anobservation target acquisition step of acquiring as observation targetlocation data, one or more pieces of location data including locationacquisition time information after an observation start time and beforean observation end time about an observation period to be observed, andincluding location information associated with observation areainformation about an observation area to be observed; a preceding andfollowing location data acquisition step of defining each piece of theobservation target location data acquired, as first location data, and,concerning each piece of the first location data, acquiring locationacquisition time information of second location data which is locationdata immediately preceding the first location data and locationacquisition time information of third location data which is locationdata immediately following the first location data, from location dataincluding the same identification information as that of the firstlocation data; a feature amount calculation step of calculating afeature amount of each piece of the observation target location data,based on at least two of the location acquisition time information ofthe first location data, the location acquisition time information ofthe second location data, and the location acquisition time informationof the third location data; and a number-of-terminals estimation step ofestimating the number of terminals located in the observation areaduring the observation period, based on the feature amount of theobservation target location data obtained by calculation, and a lengthof the observation period which is a difference between the observationstart time and the observation end time.

A number-of-terminals estimation method according to another aspect ofthe present invention is a number-of-terminals estimation methodexecuted by a number-of-terminals estimation device, comprising: alocation data acquisition step of acquiring location data includingidentification information to identify a mobile terminal, locationinformation about a location of the mobile terminal, and locationacquisition time information on a time when the location information isacquired; a preceding and following location data acquisition step ofdefining each piece of all the location data acquired, as first locationdata, and, concerning each piece of the first location data, acquiringlocation acquisition time information of second location data which islocation data immediately preceding the first location data and locationacquisition time information of third location data which is locationdata immediately following the first location data, from location dataincluding the same identification information as that of the firstlocation data; a feature amount calculation step of calculating featureamounts of respective pieces of all the location data, based on at leasttwo of the location acquisition time information of the first locationdata, the location acquisition time information of the second locationdata, and the location acquisition time information of the thirdlocation data; an observation target acquisition step of acquiring asobservation target location data, one or more pieces of location dataincluding location acquisition time information after an observationstart time and before an observation end time about an observationperiod to be observed, and including location information associatedwith observation area information about an observation area to beobserved; and a number-of-terminals estimation step of estimating thenumber of terminals located in the observation area during theobservation period, based on a feature amount of the observation targetlocation data out of the feature amounts of the respective pieces of allthe location information obtained by calculation, and a length of theobservation period which is a difference between the observation starttime and the observation end time.

A number-of-terminals estimation method according to still anotheraspect of the present invention is a number-of-terminals estimationmethod executed by a number-of-terminals estimation device, comprising:a location data acquisition step of acquiring location data includingidentification information to identify a mobile terminal, locationinformation about a location of the mobile terminal, and locationacquisition time information on a time when the location information isacquired; an observation target acquisition step of acquiring asobservation target location data, one or more pieces of location dataincluding location acquisition time information after an observationstart time and before an observation end time about an observationperiod to be observed, and including location information associatedwith observation area information about an observation area to beobserved; a preceding and following location data acquisition step ofdefining each piece of the observation target location data acquired, asfirst location data, and, concerning each piece of the first locationdata, acquiring location acquisition time information of second locationdata which is location data immediately preceding the first locationdata and location acquisition time information of third location datawhich is location data immediately following the first location data,from location data including the same identification information as thatof the first location data; a feature amount calculation step ofcalculating a feature amount of each piece of the observation targetlocation data, based on at least the location acquisition timeinformation of the second location data and the location acquisitiontime information of the third location data; and a number-of-terminalsestimation step of estimating the number of terminals located in theobservation area during the observation period, based on the featureamount of the observation target location data obtained by calculation,and a length of the observation period which is a difference between theobservation start time and the observation end time.

A number-of-terminals estimation method according to still anotheraspect of the present invention is a number-of-terminals estimationmethod executed by a number-of-terminals estimation device, comprising:a location data acquisition step of acquiring location data includingidentification information to identify a mobile terminal, locationinformation about a location of the mobile terminal, and locationacquisition time information on a time when the location information isacquired; a preceding and following location data acquisition step ofdefining each piece of all the location data acquired, as first locationdata, and, concerning each piece of the first location data, acquiringlocation acquisition time information of second location data which islocation data immediately preceding the first location data and locationacquisition time information of third location data which is locationdata immediately following the first location data, from location dataincluding the same identification information as that of the firstlocation data; a feature amount calculation step of calculating afeature amount of each piece of all the location data, based on at leastthe location acquisition time information of the second location dataand the location acquisition time information of the third locationdata; an observation target acquisition step of acquiring as observationtarget location data, one or more pieces of location data includinglocation acquisition time information after an observation start timeand before an observation end time about an observation period to beobserved, and including location information associated with observationarea information about an observation area to be observed; and anumber-of-terminals estimation step of estimating the number ofterminals located in the observation area during the observation period,based on a feature amount of the observation target location data out ofthe feature amounts of the respective pieces of all the locationinformation obtained by calculation, and a length of the observationperiod which is a difference between the observation start time and theobservation end time.

Advantageous Effect of Invention

The present invention has successfully achieved the accurate estimationof the number of terminals while correcting the influence of variationin reception intervals.

BRIEF DESCRIPTION OF DRAWINGS

FIG. 1 is a drawing showing a system configuration of a communicationsystem in the first to sixth embodiments.

FIG. 2 is a drawing showing a configuration of a number-of-terminalsestimation device in the first embodiment.

FIG. 3 is a drawing for explaining a first conception ofnumber-of-terminals estimation.

FIG. 4 is a drawing for explaining a first calculation method related tonumber-of-terminals estimation.

FIG. 5 is a flowchart showing a number-of-terminals estimation processin the first embodiment.

FIG. 6 is a flowchart showing a feature amount calculation process inthe first and second embodiments.

FIG. 7 is a drawing showing a configuration of a number-of-terminalsestimation device in the second embodiment.

FIG. 8 is a flowchart showing a number-of-terminals estimation processin the second embodiment.

FIG. 9 is a drawing for explaining a second conception ofnumber-of-terminals estimation.

FIG. 10 is a flowchart showing a feature amount calculation process inthe third embodiment.

FIG. 11 is a flowchart showing an adjustment process of variables s ande.

FIG. 12 is a drawing showing a configuration of a number-of-terminalsestimation device in the fourth embodiment.

FIG. 13 is a flowchart showing a population estimation process in thefourth embodiment.

FIG. 14 is a drawing showing a configuration of a number-of-terminalsestimation device in the fifth embodiment.

FIG. 15 is a flowchart showing a population estimation process in thefifth embodiment.

FIG. 16 is a drawing showing an output example in the populationestimation process.

FIG. 17 is a drawing showing a configuration of a number-of-terminalsestimation device in the sixth embodiment.

FIG. 18 is a drawing for explaining a combination of meshes and an areamap.

FIG. 19 is a drawing for explaining calculation of areas and area ratiosof respective divided areas.

FIG. 20 is a drawing for explaining calculation of the sum ofpopulations in divided areas in a certain mesh.

FIG. 21 is a drawing for explaining an estimated value conversionprocess in the seventh embodiment.

FIG. 22 is a drawing showing a matrix equation for conversion into anestimated population in a target output unit.

FIG. 23 is a drawing showing a configuration of a number-of-terminalsestimation device in the eighth embodiment.

FIG. 24 is a drawing for explaining an example of an unidentifiabilitysecuring process.

FIG. 25 is a drawing showing a configuration of a number-of-terminalsestimation device in the ninth embodiment.

FIG. 26 is a flowchart showing an example of a concealment process.

FIG. 27 is a drawing showing a population distribution, a populationchange, and a population composition as examples of output forms.

FIG. 28 is a drawing for explaining the second conception ofnumber-of-terminals estimation.

DESCRIPTION OF EMBODIMENTS

Embodiments of the present invention will be described below withreference to the accompanying drawings. The same portions will bedenoted by the same reference signs as much as possible, withoutredundant description.

First Embodiment

[Configuration of Communication System]

FIG. 1 is a system configuration diagram of a communication system 1 ofthe present embodiment. As shown in FIG. 1, this communication system 1is configured to include mobile terminals 100, BTSs (base transceiverstations) 200, RNCs (radio network controllers) 300, exchanges 400,various processing nodes 700, and a management center 500. Thismanagement center 500 is composed of a social sensor unit 501, apeta-mining unit 502, a mobile demography unit 503, and a visualizationsolution unit 504.

The exchanges 400 collect below-described location information on themobile terminals 100 through the BTSs 200 and RNCs 300. The RNCs 300 areable to measure locations of the mobile terminals 100 through the use ofdelay values in RRC connection request signals, during execution ofcommunication connections with the mobile terminals 100. The exchanges400 are able to receive the location information of the mobile terminals100 measured as described above, during execution of communicationconnections by the mobile terminals 100. The exchanges 400 store thereceived location information and outputs the collected locationinformation to the management center 500 at predetermining timing or inresponse to a request from the management center 500.

The various processing nodes 700 acquire the location information of themobile terminals 100 through the RNCs 300 and exchanges 400, performre-calculation of location or the like if necessary, and output thecollected location information to the management center 500 atpredetermining timing or in response to a request from the managementcenter 500.

The location information of mobile terminals 100 to be employed in thepresent embodiment can be sector numbers indicative of local sectorsacquired from location registration signals, location positioning dataobtained by a location information acquisition system such as the GPSpositioning system or PRACH PD, and so on. The location data of a mobileterminal 100 includes identification information to identify the mobileterminal (e.g., information associated with the mobile terminal, such asa line number), and location acquisition time information on a time whenthe location information is acquired, in addition to the aforementionedlocation information. When the line number is used as the identificationinformation, it is preferable to use a value associated with the linenumber (e.g., a hash of the line number or the like), instead of directuse of the line number (i.e., it is preferable to make the line numberunidentifiable). When processing according to each of user attributes isperformed using the value associated with the line number (e.g., a hashof a line number or the like) as described above, it is also necessaryto use a value associated with user-identifiable information inattribute information, instead of the user-identifiable informationitself (i.e., it is necessary to make the user-identifiable informationunidentifiable). Such unidentifiability securing process will bedescribed in detail in the eighth embodiment.

The management center 500, as described above, is configured to includethe social sensor unit 501, peta-mining unit 502, mobile demography unit503, and visualization solution unit 504, and each unit performsstatistical processing using the location information of mobileterminals 100. A below-described number-of-terminals estimation device10 (FIG. 2) can be composed, for example, of the management center 500.

The social sensor unit 501 consists of server apparatus to collect dataincluding the location information of mobile terminals 100 and others,from each exchange 400 and various processing node 700, or, off-line.This social sensor unit 501 is configured so as to be able to receivedata output at regular intervals from the exchanges 400 and variousprocessing nodes 700 or to acquire data from the exchanges 400 andvarious processing nodes 700 in accordance with timing predetermined inthe social sensor unit 501.

The peta-mining unit 502 consists of server apparatus to convert datareceived from the social sensor unit 501, into a predetermined dataformat. For example, the peta-mining unit 502 performs a sorting processusing user IDs as key or a sorting process on an area basis.

The mobile demography unit 503 consists of server apparatus to perform atotalization process on the data processed in the peta-mining unit 502,i.e., a counting process of each item. For example, the mobiledemography unit 503 is able to count the number of users located in acertain area and to totalize distributions of presence count.

The visualization solution unit 504 consists of server apparatus tovisualize the data totalized in the mobile demography unit 503. Forexample, the visualization solution unit 504 is able to perform amapping process of mapping the totalized data on a map. The dataprocessed by this visualization solution unit 504 is provided tocompanies, public agencies, individuals, or the like to be used indevelopment of shops, surveys of road traffic, countermeasures againstnatural disasters, countermeasures against environmental damage, and soon. Such statistically processed information is processed so thatindividuals or the like cannot be identified therefrom, in order toprevent invasions of privacy, as a matter of course.

Each of the social sensor unit 501, peta-mining unit 502, mobiledemography unit 503, and visualization solution unit 504 is composed ofthe server apparatus as described above, and it is needless to mentionthat each unit has an ordinary basic configuration of informationprocessing device (i.e., CPU, RAM, ROM, input devices such as keyboardand mouse, a communication device for communication with the outside, amemory device to store information, and output devices such as displayand printer), illustration of which is omitted herein.

[Configuration of Number-of-Terminals Estimation Device]

Next, the number-of-terminals estimation device according to the presentembodiment will be described. FIG. 2 shows a function blockconfiguration of the number-of-terminals estimation device 10. As shownin this FIG. 2, the number-of-terminals estimation device 10 is providedwith a location data acquisition unit 11 (location data acquisitionmeans), a storage unit 12, an observation period acquisition unit 13(observation period acquisition means), an observation area acquisitionunit 14 (observation area acquisition means), an observation targetacquisition unit 15 (observation target acquisition means), a precedingand following location data acquisition unit 16 (preceding and followinglocation data acquisition means), a feature amount calculation unit 17(feature amount calculation means), a number-of-terminals estimationunit 18 (number-of-terminals estimation means), and anumber-of-terminals output unit 19 (output means).

The functions of the respective units in the number-of-terminalsestimation device 10 in FIG. 2 will be described below. The locationdata acquisition unit 11 acquires the aforementioned location data fromthe outside and stores the location data into the storage unit 12. Thestorage unit 12 stores the location data over a plurality of times on alarge number of users (mobile terminals). The observation periodacquisition unit 13 acquires observation period information including aset of an observation start time and an observation end time. Theobservation area acquisition unit 14 acquires observation areainformation associated with one or more pieces of location information.The observation area information herein is provided, for example, assector number, latitude and longitude, geographical range (e.g., name oflocal government), or the like and the observation area acquisition unit14 is preferably provided with a database to manage correspondenceinformation between an expression form of the acquired observation areainformation and an expression form of location information (e.g.,correspondence relation information between sector numbers andlatitudes/longitudes, or the like).

The observation target acquisition unit 15 acquires as observationtarget location data, one or more pieces of location data includinglocation acquisition time information after an observation start timeand before an observation end time about an observation period to beobserved, and location information associated with observation areainformation about an observation area to be observed, from the storageunit 12. The observation target location data may be further subjectedto a narrowing process by a separately given condition (e.g., age groupsof users of mobile terminals or the like).

The preceding and following location data acquisition unit 16 acquires,concerning a piece of location data as a target on which a featureamount is calculated (which will be referred to hereinafter as “firstlocation data”), the location acquisition time information of locationdata immediately preceding the first location data (which will bereferred to hereinafter as “second location data”) and the locationacquisition time information of location data immediately following thefirst location data (which will be referred to hereinafter as “thirdlocation data”), from location data including the same identificationinformation as that of the first location data. It is not essential forthe preceding and following location data acquisition unit 16 to acquirethe whole of the second or third location data, but it is sufficient forthe preceding and following location data acquisition unit 16 toacquire, at least, the location acquisition time information in thelocation data.

In the first embodiment, the preceding and following location dataacquisition unit 16 defines the observation target location dataacquired by the observation target acquisition unit 15, as the firstlocation data and acquires the location acquisition time information ofthe second and third location data on the first location data, and thebelow-described feature amount calculation unit 17 calculates thefeature amount of the observation target location data. Namely, thefirst embodiment is an embodiment wherein the location data of thetarget on which the feature amount is calculated, is narrowed down tothe observation target location data. In contrast to it, the secondembodiment below will describe an example in which the feature amount iscalculated on each of targets of all the location data acquired.

The feature amount calculation unit 17 calculates the feature amount ofeach piece of first location data (the observation target location datain the first embodiment). For example, the feature amount calculationunit 17 calculates a difference between a location acquisition time ofthe second location data and a location acquisition time of the thirdlocation data, as the feature amount of the first location data. Whenthe location acquisition time of the second location data is an abnormalvalue, e.g., when a difference between the location acquisition time ofthe first location data and the location acquisition time of the secondlocation data is larger than a predetermined reference value (e.g., onehour) as an example, the feature amount calculation unit 17 uses as thelocation acquisition time of the second location data, a time setforward by a predetermined time (e.g., one hour) from the locationacquisition time of the first location data to calculate the featureamount of the first location data. Similarly, when the locationacquisition time of the third location data is an abnormal value, e.g.,when a difference between the location acquisition time of the firstlocation data and the location acquisition time of the third locationdata is larger than a predetermined reference value (e.g., one hour) asan example, the feature amount calculation unit 17 uses as the locationacquisition time of the third location data, a time set forward by apredetermined time (e.g., one hour) from the location acquisition timeof the first location data to calculate the feature amount of the firstlocation data. These processes in the case where the locationacquisition time of the second or third location data is an abnormalvalue are not indispensable processes, but execution of the aboveprocesses can prevent such inconvenience that when an acquisition timeduration of location data becomes abnormally long because of the mobileterminal 100 being located in an out-of-service area or because of themobile terminal 100 being in a power-off mode, the abnormally longacquisition time duration excessively affects the calculation result.

The number-of-terminals estimation unit 18 estimates the number ofterminals located in the observation area during the observation period,based on the feature amounts of the observation target location data andthe length of the observation period which is the difference between theobservation start time and the observation end time. The details will bedescribed later, but the number-of-terminals estimation unit 18estimates the number of terminals to be a numeral obtained by dividingthe sum of the feature amounts of the observation target location databy twice the length of the observation period.

The number-of-terminals output unit 19 outputs the number of terminalsobtained by the estimation. The output herein includes a variety ofoutput forms such as display output, voice output, and print output.

[Conception of Number-of-Terminals Estimation and Calculation Method]

Next, the conception of number-of-terminals estimation and calculationmethod will be described. Let us assume, like the model shown in FIG. 3,that n terminals a₁, a₂, . . . , a_(n) pass a sector S during a certainobservation period (length T) and a duration of period when eachterminal a_(i) stays in the sector S during the observation period ist_(i) (0<t_(i)≦T). In this case, the number m of terminals existing inthe sector S (in fact, an average in the observation period of thenumber m of terminals existing in the sector S) is represented byEquation (1) below.

$\begin{matrix}{m = \frac{\sum\limits_{i = 1}^{m}t_{i}}{T}} & (1)\end{matrix}$

Namely, the result of a division of the sum of the durations t_(i) ofrespective terminals a_(i) in the sector S during the observation periodby the length T of the observation period is estimated as the number mof terminals. However, true values of the durations t_(i) of therespective terminals a_(i) in the sector S during the observation periodare unobservable, but each terminal a_(i) sends signals (e.g., locationregistration signals), which are observable.

Let us assume that signals sent in the sector S during the observationperiod by terminal a_(i), are defined in chronological order as follows.

q _(i1) ,q _(i2) , . . . ,q _(ix) _(i)

(where x_(i) is a total number of signals sent in the sector S duringthe observation period by terminal a_(i)). Then the estimation of thenumber of terminals is nothing but estimating the value of m from theobserved signals q_(ij) (where j is an integer of not less than 1 andnot more than x_(i)).

Now, let us explain the calculation method of number-of-terminalsestimation on the basis of FIG. 4. It is assumed that a density ofsignals q_(ij) transmitted from terminal a_(i) (i.e., the number ofsignals per unit time) is p_(i). At this time, supposing that aprobability of transmission of signal is independent of the sector, anexpectation E(x_(i)) of a total x_(i) of signals sent in the sector Sduring the observation period by terminal a_(i) is given byE(x_(i))=t_(i)×p_(i) and therefore Equation (2) below holds as to anexpectation E(t_(i)) of the duration t_(i) of terminal a_(i) in thesector S during the observation period.

E(t _(i))=x _(i) /p _(i)  (2)

When a transmission time of each signal q_(ij) is represented by u_(ij),a density p_(ij) of signal q_(ij) is given by Equation (3) below.

p _(ij)=2/(u _(i(j+1)) −u _(i(j−1))  (3)

When the signal q_(ij) is assumed to be a signal related to the firstlocation data, the signal q_(i(j−1)) corresponds to a signal related tothe second location data and the signal q_(i(j+1)) to a signal relatedto the third location data. In the present embodiment, a differencebetween the transmission time u_(i(j−1)) of the signal q_(i(j−1))related to the second location data and the transmission time u_(i(j+1))of the signal q_(i(j+1)) related to the third location data, i.e.,(u_(i(j+1))−u_(i(j−1))) in above Equation (3) is defined as a featureamount w_(ij) on the first location data. Therefore, Equation (3) abovecan be written into the Equation (4) below. Namely, the feature amountw_(ij) can be calculated in correspondence to a reciprocal of thedensity p_(ij).

p _(ij)=2/(u _(i(j+1)) −u _(i(j−1)))=2/w _(ij)  (4)

At this time, the density p_(i) is given by the following Equation (5).

$\begin{matrix}{p_{i} = {{x_{i}\text{/}{E\left( t_{i} \right)}} = {\left( \frac{x_{i}}{\sum\limits_{j = 1}^{x_{i}}w_{ij}} \right) \times 2}}} & (5)\end{matrix}$

Therefore an estimated value E(m) of the number m of terminals can becalculated according to Equation (6) below.

$\begin{matrix}{{E(m)} = {\frac{\left( {\sum\limits_{i = 1}^{n}{\sum\limits_{j = 1}^{x_{t}}\left( {w_{ij}\text{/}2} \right)}} \right)}{T} = \frac{\left( {\sum\limits_{i = 1}^{n}{\sum\limits_{j = 1}^{x_{t}}w_{ij}}} \right)}{2T}}} & (6)\end{matrix}$

When it is assumed as shown in the example of FIG. 4 that in theobservation period and in the period when the terminal a_(i) stays inthe sector S, the terminal a_(i) transmits signals q_(i1), q_(i2), andq_(i3), that the terminal a_(i) transmits a signal q_(i0) immediatelybefore the signal q_(i1) and transmits a signal q_(i4) immediately afterthe signal q_(i3), and that the transmission times of the signalsq_(i0), q_(i1), q_(i2), q_(i3), and q_(i4) are u_(i0), u_(i1), u_(i2),u_(i3), and u_(i4), respectively, the aforementioned conception isequivalent to estimating the duration t_(i) of the terminal a_(i) in thesector S during the observation period to be a duration from (a midpointbetween u_(i0) and u_(i1)) to (a midpoint between u_(i3) and u_(i4)).The terminal a_(i) transmits the signal q_(i4) while staying in thesector S, but not during the observation period. However, in order tomaintain unbiasedness of the estimated value of the duration t_(i), aprocess without an estimation of estimating the end time of the durationt_(i) to be the same as the end time of the observation period T will bedescribed as an example herein.

[Number-of-Terminals Estimation Process]

A number-of-terminals estimation process according to anumber-of-terminals estimation method of the present invention will bedescribed below. It is assumed herein as an example that the locationinformation in the location data of a mobile terminal given hereinincludes a sector number of a sector in which the mobile terminal stays.

As shown in FIG. 5, first, the location data acquisition unit 11acquires the location data from the outside and stores the location datainto the storage unit 12 (step S1 in FIG. 5). Through this step, thestorage unit 12 comes to store the location data over a plurality oftimes on a large number of users (mobile terminals). After execution ofthe process in step S1, processes in step S2 and subsequent steps may beexecuted after a lapse of some time. Namely, step S1 may be executed asa preparation step before the processes in step S2 and subsequent steps.

Next, the observation period acquisition unit 13 acquires theobservation period information including a set of an observation starttime and an observation end time and the observation area acquisitionunit 14 acquires the observation area information associated with one ormore pieces of location information (step S2). It is assumed herein thata set of observation start time T1 and observation end time T2 isacquired as the observation period information and that a sector numberS is acquired as the observation area information.

Next, the observation target acquisition unit 15 acquires, as theobservation target location data, one or more pieces of location dataincluding the location acquisition time information after theobservation start time T1 and before the observation end time T2 andincluding the location information associated with the sector number Sas the observation area information (e.g., location data the locationinformation of which is the sector number S), from the storage unit 12(step S3). Namely, the observation target acquisition unit 15 acquiresthe location data meeting the following conditions, as the observationtarget location data.

Condition 1: the location acquisition time is after the observationstart time T1 and before the observation end time T2. Namely, thelocation acquisition time is within the observation period.Condition 2: the location information is sector S.

Next, the processes in steps S4 and S5 below are executed for each pieceof the acquired observation target location data. In step S4, concerninga piece of location data (first location data) as a target forcalculation of the feature amount out of the observation target locationdata, the preceding and following location data acquisition unit 16acquires the location acquisition time information of the location data(second location data) immediately preceding the first location data andthe location acquisition time information of the location data (thirdlocation data) immediately following the first location data, in view oftheir location acquisition times, from the location data including thesame identification information as that of the first location data. Itis noted herein that it is not essential for the preceding and followinglocation data acquisition unit 16 to acquire the whole of the second andthird location data, but it is sufficient for the preceding andfollowing location data acquisition unit 16 to acquire the locationacquisition time information in the second and third location data.

Then, in step S5 the feature amount calculation unit 17 calculates thefeature amount of the first location data. The content of the processwill be described using FIG. 6. It is assumed herein that the locationacquisition times of the first, second, and third location data are t1,t2, and t3, respectively. It is also assumed that a reference value A(e.g., one hour) is defined as a predetermined reference value of areference to determine that the location acquisition time t2 of thesecond location data is an abnormal value (a reference value about adifference between the location acquisition times of the first andsecond location data) and that a reference value B (e.g., one hour) isdefined as a predetermined reference value of a reference to determinethat the location acquisition time t3 of the third location data is anabnormal value (a reference value about a difference between thelocation acquisition times of the first and third location data).

The feature amount calculation unit 17 calculates the difference Dabetween the location acquisition times of the first and second locationdata (i.e., the difference between times t1 and t2), and the differenceDb between the location acquisition times of the first and thirdlocation data (i.e., the difference between times t1 and t3) (step S11in FIG. 6). Then the feature amount calculation unit 17 determineswhether the difference Da between the location acquisition times of thefirst and second location data is larger than the predeterminedreference value A (e.g., one hour) (step S12); if the difference Da islarger than the reference value A, the feature amount calculation unit17 defines a time set backward by a predetermined time (e.g., one hour)from the location acquisition time t1 of the first location data, as thelocation acquisition time t2 of the second location data (step S13).Next, the feature amount calculation unit 17 determines whether thedifference Db between the location acquisition times of the first andthird location data is larger than the predetermined reference value B(e.g., one hour) (step S14); if the difference Db is larger than thereference value B, the feature amount calculation unit 17 defines a timeset forward by a predetermined time (e.g., one hour) from the locationacquisition time t1 of the first location data, as the locationacquisition time t3 of the third location data (step S15). Then thefeature amount calculation unit 17 calculates a difference between thelocation acquisition time t2 of the second location data and thelocation acquisition time t3 of the third location data, as a featureamount of the first location data (step S16). The above completes theprocesses in steps S4 and S5 for a piece of observation target locationdata (first location data).

Thereafter, the aforementioned processes in steps S4 and S5 are executedfor each piece of the observation target location data, and the flowgoes to step S7 after the execution of the processes is completed forall pieces of the observation target location data (with an affirmativejudgment in step S6).

In step S7, the number-of-terminals estimation unit 18 estimates thenumber of terminals to be a numeral obtained by dividing the sum of thefeature amounts w_(ij) on the observation target location data by twicethe length T of the observation period, as shown in the aforementionedEquation (6). As apparent from Equation (6), the number-of-terminalsestimation unit 18 may estimate the number of terminals to be a numeralobtained by dividing each of the feature amounts w_(ij) on theobservation target location data by 2, calculating the sum of (featureamounts w_(ij)/2), and then dividing the obtained sum by the length T ofthe observation period. However, the number of divisions isoverwhelmingly smaller in the calculation method of dividing the sum ofthe feature amounts w_(ij) on the observation target location data bytwice the length T of the observation period as in the presentembodiment, which provides the advantage of reduction in processingload.

Furthermore, the number-of-terminals output unit 19 outputs the numberof terminals obtained by the estimation (step S8).

Since the first embodiment described above involves performing thecorrection using the acquisition time information of the preceding andfollowing location data in estimating the number of terminals using thelocation data, the number of terminals can be accurately estimated whilecorrecting the influence of variation in reception intervals.

Since the processes in the case where the location acquisition time ofthe second or third location data is the abnormal value as describedabove are carried out in the calculation process of feature amount, whenthe acquisition time interval of location data becomes abnormally longbecause of the mobile terminal 100 being located in an out-of-servicearea or because of the mobile terminal 100 being in a power-off mode, itbecomes feasible to prevent the abnormally long acquisition timeinterval from excessively affecting the calculation result.

Second Embodiment

The foregoing first embodiment described the example in which thelocation data of the target for calculation of the feature amount wasnarrowed down to the observation target location data, whereas thesecond embodiment below will describe an example in which the featureamounts are calculated for targets of all pieces of the acquiredlocation data, i.e., example in which the feature amounts arepreliminarily calculated for all pieces of the location data beforeexecution of the number-of-terminals estimation and in which the numberof terminals is estimated using the feature amounts of the observationtarget location data among them. The system configuration of thecommunication system in the second embodiment is the same as the systemconfiguration in the first embodiment shown in FIG. 1, and therefore thedescription of the same system configuration is omitted herein.

As shown in FIG. 7, the number-of-terminals estimation device 10according to the second embodiment is provided with the same componentsas the number-of-terminals estimation device of the first embodiment(FIG. 2) and the functions of the respective components are much thesame; therefore, it will be described with focus on differences from thenumber-of-terminals estimation device of the first embodiment.

The observation target acquisition unit 15 in the second embodimentacquires as the observation target location data, one or more pieces oflocation data including the location acquisition time information afterthe observation start time and before the observation end time about theobservation period to be observed and including the location informationassociated with the observation area information about the observationarea to be observed, and thereafter outputs the observation targetlocation data to the number-of-terminals estimation unit 18.

The preceding and following location data acquisition unit 16 defineseach of all pieces of the location data acquired by the location dataacquisition unit 11, as the first location data and acquires thelocation acquisition time information of the second location data(immediately-preceding location data) and the third location data(immediately-following location data) about the first location data. Thelocation data acquired by the location data acquisition unit 11 may bedata stored in the storage unit 12 after acquired by the location dataacquisition unit 11, or data transmitted from the location dataacquisition unit 11 to the preceding and following location dataacquisition unit 16, without being stored in the storage unit 12.

The feature amount calculation unit 17 defines each of all pieces of thelocation data acquired by the location data acquisition unit 11, as thefirst location data and calculates the feature amount of the firstlocation data. Since the result of this calculation becomes a hugeamount of data, the feature amount calculation unit 17 is preferablyprovided with a feature amount storage unit 17A for storage of featureamounts as the calculation result as shown in FIG. 7, and the featureamount storage unit 17A stores the feature amounts as the calculationresult. The second embodiment is the same as the first embodiment inthat the feature amount calculation unit 17 calculates the differencebetween the location acquisition times of the second and third locationdata as the feature amount of the first location data and in that thedevice performs the processes in the case where the location acquisitiontime of the second or third location data is an abnormal value as shownin FIG. 6.

The number-of-terminals estimation unit 18 extracts the feature amountsof the observation target location data received from the observationtarget acquisition unit 15, from the feature amounts of all pieces oflocation data preliminarily calculated and stored in the feature amountstorage unit 17A, and estimates the number of terminals located in theobservation area during the observation period, based on the featureamounts of the observation target location data and the differencebetween the observation start time and the observation end time (thelength of the observation period). Specifically, as in the firstembodiment, the number-of-terminals estimation unit 18 estimates thenumber of terminals to be a numeral obtained by dividing the sum of thefeature amounts of the observation target location data by twice thelength of the observation period.

The number-of-terminals estimation process in the second embodiment willbe described below. It is assumed herein that the location informationin the location data of each mobile terminal is a sector number of asector in which the mobile terminal stays.

As shown in FIG. 8, first, the location data acquisition unit 11acquires the location data from the outside and stores the location datainto the storage unit 12 (step S21 in FIG. 8). However, the storage ofthe location data into the storage unit 12 is not indispensable, but thelocation data may be transmitted directly from the location dataacquisition unit 11 to the preceding and following location dataacquisition unit 16, followed by execution of below-described step S22.The processes in step S22 and subsequent steps may be executed with sometime interval, after execution of the process in step S21.

Next, the processes in steps S22 to S24 below are carried out for eachof all pieces of the location data acquired. In step S22, concerning apiece of location data (first location data) as a target for calculationof the feature amount, the preceding and following location dataacquisition unit 16 acquires the location acquisition time informationof the location data (second location data) immediately preceding thefirst location data and the location acquisition time information of thelocation data (third location data) immediately following the firstlocation data in view of the location acquisition times, from thelocation data including the same identification information as that ofthe first location data. It is not essential for the preceding andfollowing location data acquisition unit 16 to acquire the whole of thesecond and third location data, but it is sufficient for the precedingand following location data acquisition unit 16 to acquire the locationacquisition time information in the second and third location data. Thenin step S23, the feature amount calculation unit 17 calculates thefeature amount of the first location data in accordance with theprocedure shown in FIG. 6 which is the same as in the first embodiment.Since the process in step S23 is the same as the process in step S5 inthe first embodiment described above, the description thereof is omittedherein. Thereafter, the feature amount obtained in step S23 is stored inthe feature amount storage unit 17A (step S24).

The above completes the processes in steps S22-S24 on a piece ofobservation target location data (first location data).

Thereafter, the processes in steps S22-S24 are executed for each of allpieces of the location data. After the processes in steps S22-S24 arecompleted for all pieces of the location data (with an affirmativejudgment in step S25), the feature amounts of all pieces of the locationdata have been calculated and stored in the feature amount storage unit17A. In this manner, the feature amounts of all pieces of the locationdata can be preliminarily calculated and stored before execution of thenumber-of-terminals estimation.

In next step S26, the observation period acquisition unit 13 acquiresthe observation period information including a set of an observationstart time and an observation end time, and the observation areaacquisition unit 14 acquires the observation area information associatedwith one or more pieces of location information. It is assumed hereinthat a set of observation start time T1 and observation end time T2 isacquired as the observation period information and that a sector numberS is acquired as the observation area information.

Next, the observation target acquisition unit 15 acquires as theobservation target location data, one or more pieces of location dataincluding the location acquisition time information after theobservation start time T1 and before the observation end time T2 andincluding the location information associated with the sector number Sas the observation area information (e.g., the location information ofwhich is the sector number S), from the storage unit 12 (step S27).Namely, the observation target acquisition unit 15 acquires the locationdata meeting the following conditions, as the observation targetlocation data.

Condition 1: the location acquisition time is after the observationstart time T1 and before the observation end time T2. Namely, thelocation acquisition time is within the observation period.Condition 2: the location information is the sector S.The acquisition of the observation target location data may be carriedout as follows. Namely, the feature amount calculation unit 17associates the feature amount obtained by the calculation, with thecalculation target location data (the foregoing first location data) andstores the location data with the feature amount after associated, inthe feature amount storage unit 17A. Then the number-of-terminalsestimation unit 18 acquires the observation period information and theobservation area information via the observation target acquisition unit15, and retrieves the location data with the feature amount matching theconditions of the observation period and the observation area, from thefeature amount storage unit 17A, to acquire the location data with thefeature amount as the observation target location data.

Returning to FIG. 8, in next step S28 the number-of-terminals estimationunit 18 estimates the number of terminals to be a numeral obtained bydividing the sum of the feature amounts w_(ij) on the observation targetlocation data by twice the length T of the observation period, as shownin Equation (6) above. As apparent from Equation (6), thenumber-of-terminals estimation unit 18 may estimate the number ofterminals to be a numeral obtained by dividing each of the featureamounts w_(ij); on the observation target location data by 2,calculating the sum of (feature amounts w_(ij)/2), and dividing theobtained sum by the length T of the observation period. However, thenumber of divisions is overwhelmingly smaller in the calculation methodof dividing the sum of the feature amounts w_(ij) on the observationtarget location data by twice the length T of the observation period asin the present embodiment, and thus the calculation method in thepresent embodiment provides the advantage of reduction in processingload.

Furthermore, the number-of-terminals output unit 19 outputs the numberof terminals obtained by the estimation (step S29).

The second embodiment described above involves performing the correctionusing the acquisition time information of the preceding and followinglocation data in estimating the number of terminals using the locationdata, as in the first embodiment, whereby the number of terminals can beaccurately estimated while correcting the influence of variation inreception intervals.

In particular, since the second embodiment involves calculating andstoring the feature amounts of all pieces of location data in advancebefore the execution of the number-of-terminals estimation, thenumber-of-terminals estimation device has the advantage of reduction intime from the acquisition of the observation period information and theobservation area information and the start of the number-of-terminalsestimation process to the acquisition of the number of terminals as theestimation result.

In the processing of FIG. 8, it is not essential to execute theprocesses in steps S26-S27 after step S25, and the processes in stepsS22 to S25 may be concurrently executed in parallel with the processesin steps S26-S27.

Third Embodiment

The third embodiment will describe the second technique about thenumber-of-terminals estimation and a feature amount calculation processbased on the second technique. Since the configurations of thecommunication system and the number-of-terminals estimation device inthe third embodiment are the same as in the first and secondembodiments, the description thereof is omitted herein.

FIG. 9 shows a drawing concerning the second conception ofnumber-of-terminals estimation. In this FIG. 9, q_(ij) represents thelocation data generated by terminal and among them, q_(i1), q_(i2), andq_(i3) indicate the location data generated by terminal a_(i) whilestaying in the sector S during the observation period. It is assumedherein that q_(i1) includes location registration information generateddue to a crossing of the terminal a_(i) across a border of a locationregistration area (Location Area) (which will be referred to hereinafteras “LA-crossing location registration information”), and it will bereferred to hereinafter as “LA-crossing location registrationinformation q_(i1).” In this case, since it can be determined that theterminal a_(i) entered the sector S at a time of generation of theLA-crossing location registration information q_(i1), we can alsoestablish another conception to define the feature amount with w_(i1) onthe LA-crossing location registration information q_(i1), as adifference between the generation time of the LA-crossing locationregistration information q_(i1) and the generation time of theimmediately-following location data q_(i2), instead of the differencebetween the generation time of the immediately-preceding location dataq_(i0) and the generation time of the immediately-following locationdata q_(i2) as employed in the aforementioned first and secondembodiments.

Based on this conception, the duration t_(i) of period when the terminala_(i) stays in the sector S during the observation period is a durationindicated by a thick solid line in FIG. 9, which is shorter by(difference between the generation time of the LA-crossing locationregistration information q_(i1) and the generation time of theimmediately-preceding location data q_(i0)/2) than the duration t_(i) inthe first and second embodiments (the duration indicated by a dashedline in FIG. 9).

Furthermore, as shown in FIG. 28, the location data q_(i4) is assumed toinclude the LA-crossing location registration information and it will bereferred to hereinafter as “LA-crossing location registrationinformation q_(i4).” In this case, it can be determined that theterminal a_(i) leaves the sector S at a time of generation of theLA-crossing location registration information q_(i4). For this reason,when the feature amount w_(i3) is calculated on the location data q_(i3)immediately preceding the LA-crossing location registration informationq_(i4), the feature amount w_(i3) on the location data q_(i3) becomeslonger by (difference between the generation time of the location dataq_(i3) and the generation time of the immediately-following LA-crossinglocation registration information q_(i4)/2). Namely, the duration t_(i)of period when the terminal a_(i) stays in the sector S during theobservation period is a duration indicated by a thick solid line in FIG.28, which is longer by (difference between the generation time of thelocation data q_(i3) and the generation time of theimmediately-following LA-crossing location registration informationq_(i4)/2) than the duration t_(i) in the first and second embodiments(the duration indicated by a dashed line in FIG. 28).

The feature amount calculation process based on the second conception ofthe number-of-terminals estimation as described above will be describedusing FIG. 10. The location data as a target for calculation of featureamount will be referred to hereinafter as “calculation target locationdata.”

As shown in FIG. 10, the feature amount calculation unit 17 firstdetermines whether the calculation target location data includes theLA-crossing location registration information, for example, by serviceclass information included in the calculation target location data (stepS31). In this step, when the calculation target location data includesthe LA-crossing location registration information, the feature amountcalculation unit 17 sets the location acquisition time of thecalculation target location data to a first variable s for calculationof feature amount (which will be referred to hereinafter as “variables”) (step S32); when the calculation target location data does notinclude the LA-crossing location registration information, the featureamount calculation unit 17 sets a midpoint time between the locationacquisition time of the calculation target location data and thelocation acquisition time of the immediately-preceding location data tothe variable s (step S33).

Next, the feature amount calculation unit 17 determines whether theimmediately-following location data includes the LA-crossing locationregistration information, for example, by service class informationincluded in the immediately-following location data (step S34). In thisstep, when the immediately-following location data includes theLA-crossing location registration information, the feature amountcalculation unit 17 sets the location acquisition time of theimmediately-following location data to a second variable e forcalculation of feature amount (which will be referred to hereinafter as“variable e”) (step S35); when the immediately-following location datadoes not include the LA-crossing location registration information, thefeature amount calculation unit 17 sets a midpoint time between thelocation acquisition time of the calculation target location data andthe location acquisition time of the immediately-following location datato the variable e (step S36). It is not essential to perform thedetermination processes in steps S31, S34 above on the basis of theservice class information, but they may be performed based on otherinformation. For example, it is also possible to adopt a scheme in whicharea information indicative of ranges of location registration areas ispreliminarily retained and the determination processes are carried outbased on the location information of the calculation target locationdata and the immediately-following location data, and the areainformation.

Next, the feature amount calculation unit 17 performs an adjustmentprocess of the variables s, e shown in FIG. 11 (step S37). It is assumedherein that the location acquisition time of the calculation targetlocation data is t1, a reference value C (e.g., 0.5 hour) is defined asa predetermined reference value of a reference to determine that thevariable s is an abnormal value, and a reference value D (e.g., 0.5hour) is defined as a predetermined reference value of a reference todetermine that the variable e is an abnormal value.

The feature amount calculation unit 17 calculates a difference Dcbetween the variable s and the time t1 and a difference Dd between thevariable e and the time t1 (step S41 in FIG. 11). Then the featureamount calculation unit 17 determines whether the difference Dc betweenthe variable s and the time t1 is larger than the predeterminedreference value C (e.g., 0.5 hour) (step S42); if the difference Dc islarger than the reference value C, the feature amount calculation unit17 sets a time set backward by a predetermined time (e.g., 0.5 hour)from the time t1, to the variable s (step S43). Next, the feature amountcalculation unit 17 determines whether the difference Dd between thevariable e and the time t1 is larger than the predetermined referencevalue D (e.g., 0.5 hour) (step S44); if the difference Dd is larger thanthe reference value D, the feature amount calculation unit 17 sets atime set forward by a predetermined time (e.g., 0.5 hour) from the timet1, to the variable e (step S45). By carrying out this adjustmentprocess of the variables s, e, when the acquisition time interval oflocation data becomes abnormally long because of the mobile terminal 100being located in an out-of-service area or because of the mobileterminal 100 being in a power-off mode, it is feasible to prevent theabnormally long acquisition time interval from excessively affecting thecalculation result.

Next, returning to FIG. 10, the feature amount calculation unit 17calculates a value of twice (variable e-variable s) as a feature amountof the calculation target location data (step S38). The feature amountof the calculation target location data is obtained through the aboveprocessing.

The third embodiment described above can obtain the feature amount withhigh accuracy while taking account of the point that when at least oneof the calculation target location data and the immediately-followinglocation data includes the LA-crossing location registrationinformation, the entrance into the sector S or the exit from the sectorS is determined to have occurred at the time of generation of theLA-crossing location registration information.

The feature amount calculation technique described in the thirdembodiment is also applicable to the case where the feature amounts arecalculated by narrowing down the location data to the observation targetlocation data and where the number of terminals is estimated by thefeature amounts obtained, as in the first embodiment, and to the casewhere the feature amounts are preliminarily calculated for all pieces ofthe location data and where the number of terminals is estimated usingthe feature amounts of the observation target location data among themas in the second embodiment.

Fourth Embodiment

The fourth and fifth embodiments below will describe examples in which apopulation is estimated using a factor to convert the number ofterminals obtained by the estimation, into a population (which will bereferred to hereinafter as “scaling factor”). The fourth embodiment ofthem will describe an embodiment in which each feature amount ismultiplied by the scaling factor and a population is determined using anaggregate value of results of the multiplication, and the fifthembodiment will describe an embodiment in which the feature amounts aretotalized in respective population estimation units (e.g., in respectiveattributes or time zones), the aggregate values are multiplied byscaling factors according to the population estimation units, andpopulations are determined using the multiplication results.

The embodiments of the fourth embodiment and subsequent embodiments willbe described based on the process of preliminarily calculating thefeature amounts of all pieces of location data and estimating thepopulation or the number of terminals using the feature amounts of theobservation target location data among them. It is, however, noted thatthe embodiments are also applicable to the process of calculating thefeature amounts of the narrowed observation target location data andestimating the population or the number of terminals using them.

As shown in FIG. 12, the function block configuration of thenumber-of-terminals estimation device 10 in the fourth embodiment isdifferent in below-described points from the function blockconfiguration of the number-of-terminals estimation device in the secondembodiment (FIG. 7), and thus the different points will be describedbelow. The number-of-terminals estimation device 10 is provided with apopulation estimation unit 21 to estimate a population on the basis ofthe feature amounts, instead of the number-of-terminals estimation unit18, and is provided with a population output unit 22 to output theestimated value by the population estimation unit 21, instead of thenumber-of-terminals output unit 19. However, the “number-of-terminalsestimation means” in the scope of claims corresponds to thenumber-of-terminals estimation unit 18 and the population estimationunit 21, and the “output means” to the number-of-terminals output unit19 and the population output unit 22.

Furthermore, the number-of-terminals estimation device 10 is providedwith an attribute and scaling factor storage unit 23 (scaling factorstorage means) storing attribute information of each mobile terminaluser and scaling factors for respective attributes preliminarilyobtained. Using the user identification information of location data(e.g., hashed phone number) as key, the feature amount calculation unit17 retrieves the attribute information of user and the scaling factorabout the attribute information from the attribute and scaling factorstorage unit 23, associates the location data with the calculatedfeature amount and the retrieved attribute information and scalingfactor, and stores the location data after associated, into the featureamount storage unit 17A.

Next, the processing by the number-of-terminals estimation device 10 inthe fourth embodiment will be described using FIG. 13. The processing inthe fourth embodiment shown in FIG. 13 is different in below-describedpoints from the processing in the second embodiment (FIG. 8) and thusthe different points will be described below.

As shown in FIG. 13, after execution of the feature amount calculationprocess in step S23, step S24A is carried out as follows: using the useridentification information of location data (e.g., hashed phone number)as key, the feature amount calculation unit 17 retrieves the attributeinformation of user and the scaling factor about the attributeinformation from the attribute and scaling factor storage unit 23,associates the location data with the calculated feature amount and theretrieved attribute information and scaling factor, and stores thelocation data after associated, into the feature amount storage unit17A. The processes in steps S22 to S24A in FIG. 13 are carried out inorder on all pieces of the location data, whereby the location data inassociation with the respective feature amounts and with the respectiveattribute information and scaling factors is stored in the featureamount storage unit 17A.

Thereafter, step S26 is carried out to acquire the observation periodinformation and the observation area information, and in step S27thereafter, the population estimation unit 21 receives the observationperiod information and the observation area information via theobservation target acquisition unit 15 and acquires the location datameeting the conditions of the observation period information and theobservation area information (i.e., the observation target locationdata) from the feature amount storage unit 17A. Then in step S28A thepopulation estimation unit 21 multiplies the feature amounts of therespective pieces of observation target location data by the scalingfactors and estimates a population in the observation area during theobservation period to be a value obtained by dividing the sum of theobtained multiplication results by (observation length×2). Furthermore,the population estimation unit 21 totalizes the above multiplicationresults on the respective pieces of observation target location data onan attribute-by-attribute basis, based on the attribute informationassociated with the respective pieces of observation target locationdata and the acquisition time information in the respective pieces ofobservation target location data, and estimates populations inrespective attributes to be values obtained by dividing the total valuesby (observation length×2). The example of estimating the populations inrespective attributes was described herein, but the units of estimationof populations do not have to be limited to the attributes, and may beplaces, time zones, or the like. These estimation units (attributes,places, time zones, etc.) will be called below “population estimationunits.”

Furthermore, in next step S29A the population output unit 22 outputs thepopulation in the observation area during the observation period and thepopulations in the respective population estimation units, which wereobtained by the estimation in step S28A. In this step, for example asshown in FIG. 16, the population output unit 22 can output a populationestimated on an observation area (represented by “estimated population”in FIG. 16), and populations in respective attributes such as genders,age groups, and addresses. It is also possible to adopt a combinationalcondition of multiple attributes (e.g., a condition of “women residingin Tokyo” as a combination of a gender and an address). The outputherein includes a variety of output forms such as display output, voiceoutput, and print output.

The fourth embodiment described above allows the device to obtain thepopulation in the observation area during the observation period and thepopulations in respective population estimation units (e.g., attributesor time zones).

The foregoing steps S28A, S29A showed the example to estimate and outputboth of the population in the observation area during the observationperiod and the populations in respective population estimation units,but it should be noted that the estimation and output of the both is notessential and that it is also possible to estimate and output one ofthem.

The fourth embodiment described the example in which the scaling factorswere preliminarily obtained, but the scaling factors may be acquired asfollows. An scaling factor to be used herein as an example can be areciprocal of a product of a presence rate and a penetration rate ofterminal (i.e., a ratio of a presence count to a population). The“presence rate” herein means a rate of a presence count to the number ofsubscriptions, and the “penetration rate” a rate of the number ofsubscriptions to a population. It is preferable to derive such anscaling factor in each of the aforementioned scaling factor calculationunits, but it is not essential. The scaling factors may be derived, forexample, using the number of terminals (presence count) estimated basedon the feature amounts and the length of the observation period asdescribed below. The feature amounts are determined from the locationdata by the techniques as described in the first to third embodiments,the numbers of terminals in respective scaling factor calculation unitsare totalized based on the feature amounts and the length of theobservation period to obtain user count pyramid data, and populationpyramid data in the same scaling factor calculation units preliminarilyobtained as statistical data (e.g., The Basic Resident Register or thelike) is acquired. Then an acquisition rate of location data in each ofthe scaling factor calculation units (i.e., presence count/population ineach unit) is calculated with the user count pyramid data and thepopulation pyramid data. The “acquisition rate of location data (i.e.,presence count/population)” corresponds to the aforementioned “productof a presence rate and a penetration rate of terminal.” Reciprocals ofthe “acquisition rates of location data” obtained in this manner can bederived as the scaling factors. The scaling factor calculation units forcalculation of the scaling factors to be adopted herein may be, forexample, prefectures of addresses, age groups at intervals of five yearsor ten years, genders, one-hour zones as time zones, and so on, or maybe a combination of two or more of them. For example, when an scalingfactor calculation unit is “men in their twenties residing in Tokyo,”the location data corresponding to men in their twenties residing inTokyo (i.e., address information in user attributes of which is Tokyo)is extracted in the whole of Japan and the number of terminals iscounted to obtain user count pyramid data, and the population pyramiddata about men in their twenties residing in Tokyo is acquired from thestatistical data. In the acquisition of the above user count pyramiddata, the condition of “residing in Tokyo” is not to extract only thelocation data of users residing in Tokyo, but to extract the locationdata the address information in user attributes of which is Tokyo. Thenan acquisition rate of location data (i.e., presence count/population)in the scaling factor calculation unit (men in their twenties residingin Tokyo herein) is calculated from the user count pyramid data and thepopulation pyramid data, and a reciprocal of the “acquisition rate oflocation data” thus obtained can be derived as an scaling factor. In thepresent specification the scaling factor calculation units are describedas equal to the population estimation units, but this is just anexample, without need for being limited to this example.

The fourth embodiment described the process of obtaining the populationon the basis of the second embodiment, but the fourth embodiment is alsoapplicable to the aforementioned first and third embodiments.

Fifth Embodiment

The fifth embodiment will describe an embodiment in which the featureamounts are totalized in respective population estimation units (e.g.,respective attributes or time zones) and the total values are multipliedby scaling factors according to the respective population estimationunits to obtain populations.

As shown in FIG. 14, the function block configuration of thenumber-of-terminals estimation device 10 in the fifth embodiment issimilar to that of the number-of-terminals estimation device in theaforementioned fourth embodiment (FIG. 12), but the present embodimentis different from the fourth embodiment in that, instead of the featureamount calculation unit 17, the population estimation unit 21 retrievesthe scaling factors from the attribute and scaling factor storage unit23 and estimates the population through the use of the scaling factors.Namely, the processes by the feature amount calculation unit 17 and thepopulation estimation unit 21 are different from those in the fourthembodiment, and the differences will be described using FIG. 15.

As shown in FIG. 15, after execution of the feature amount calculationprocess in step S23, step S24B is carried out as follows: the featureamount calculation unit 17 specifies attribute information of a user oflocation data from the location data with the use of the useridentification information (e.g., hashed phone number) as key, retrievesthe attribute information from the attribute and scaling factor storageunit 23, associates the location data with the calculated feature amountand the retrieved attribute information, and stores the location dataafter associated, in the feature amount storage unit 17A. The processesin steps S22 to S24B in FIG. 15 are executed in order on all pieces oflocation data, whereby the location data associated with the respectivefeature amounts and attribute information is stored in the featureamount storage unit 17A.

Thereafter, step S26 is carried out to acquire the observation periodinformation and the observation area information, and thereafter in stepS27 the population estimation unit 21 acquires the location data meetingthe conditions of the observation period information and the observationarea information (i.e., the observation target location data) from thefeature amount storage unit 17A. Then in step S28B the populationestimation unit 21 performs the population estimation process using thescaling factor as described below.

First, the population estimation unit 21 specifies the observationtarget location data meeting a condition of a population estimation unit(attribute or time zone), out of the acquired observation targetlocation data, totalizes the feature amounts of the specifiedobservation target location data (step S51), retrieves the scalingfactor about the population estimation unit from the attribute andscaling factor storage unit 23, multiplies the feature amount totalvalue by the scaling factor, and estimates a population in thepopulation estimation unit to be a value obtained by dividing themultiplication result (scaling factor×feature amount total value) by(observation period length×2) (step S52). Then the above steps S51 toS52 are executed for each of population estimation units and, aftercompletion of execution for all the population estimation units, theflow goes to step S54. At this point, populations in the respectivepopulation estimation units are obtained as the estimation result.Furthermore, the populations in the respective population estimationunits are summed up and the sum is estimated as a population in theobservation area during the observation period (step S54).

The above step S28B results in obtaining the population in theobservation area during the observation period and the populations inthe respective population estimation units. Then, in the next step S29Athe population output unit 22 outputs the population in the observationarea during the observation period and the populations in the respectivepopulation estimation units, which were obtained by the estimation instep S28B.

The fifth embodiment as described above allows the device to obtain thepopulation estimated on the observation area (represented by “estimatedpopulation” in FIG. 16) and the populations in the respective populationestimation units such as attributes or time zones, for example, as shownin FIG. 16.

As in the case of the fourth embodiment, steps S28A and S29 showed theexample in which both of the population in the observation area duringthe observation period and the populations in the respective populationestimation units were estimated and output, but it should be noted thatthe estimation and output of both is not essential and that it is alsopossible to estimate and output one of them. The fifth embodimentdescribed the process to obtain the population on the basis of thesecond embodiment, but the fifth embodiment is also applicable to theaforementioned first and third embodiments.

Sixth Embodiment

The sixth and seventh embodiments below will describe examples in whichestimated values (populations or the numbers of terminals) in respectiveareas of totalization units are converted into estimated values inrespective output units (meshes as an example herein). The sixthembodiment of them will describe the processing in an environment inwhich there are communication regions of outdoor stations using a singlefrequency band, whereas the seventh embodiment will describe theprocessing in an environment in which there are two or moregeographically overlapping communication regions out of communicationregions of indoor stations and communication regions of outdoor stationsusing respective frequency bands with different coverage areas. When theoutput units are the same as the totalization units, the conversionprocesses described in the sixth and seventh embodiments below are notneeded.

The sixth embodiment will describe the number-of-terminals estimationdevice and processing thereof in which the conversion function intoestimated values in respective meshes is added to thenumber-of-terminals estimation device to estimate the populationaccording to the fourth embodiment.

As shown in FIG. 17, the function block configuration of thenumber-of-terminals estimation device 10 in the sixth embodiment is aconfiguration in which a conversion unit 24 (conversion means) is addedbetween the population estimation unit 21 and the population output unit22 in the function block configuration of the number-of-terminalsestimation device in the fourth embodiment (FIG. 12). The conversionunit 24 converts the populations in respective areas acquired by theestimation by the population estimation unit 21, into populations inrespective meshes by below-described processing.

The processing of the conversion unit 24 will be described below indetail on the basis of FIG. 18. Part (a) of FIG. 18 is a drawing showingarea ranges of respective areas, part (b) of FIG. 18 a drawing showingmeshes, and part (c) of FIG. 18 a composite chart as a combination ofthe areas and the meshes.

The conversion unit 24 combines an area map (cf. (a) of FIG. 18)reproduced based on preliminarily-stored area border information, withtwo-dimensional meshes (cf. (b) of FIG. 18) reproduced based on apredetermined division rule, to obtain the composite chart as shown in(c) of FIG. 18. Next, the conversion unit 24 divides each area by meshborders in the foregoing composite chart. For example, as shown in FIG.19, area A in (a) of FIG. 18 is divided into four divided areas A-1,A-2, A-3, and A-4 by mesh borders. Then the conversion unit 24calculates the areas of the respective divided areas and calculates arearatios of the respective divided areas. For example, as shown in FIG.19, supposing that the areas of the divided areas A-1, A-2, A-3, and A-4are calculated as 10 m², 50 m², 100 m², and 40 m², respectively, thearea ratios (e.g., percentages) of the respective divided areas A-1,A-2, A-3, and A-4 are calculated as 5%, 25%, 50%, and 20%.

It is not indispensable for the conversion unit 24 to calculate the arearatios of the respective divided areas. It is possible, for example, toadopt a configuration in which the area ratios of the respective dividedareas are preliminarily determined and in which the conversion unit 24can retrieve the information on the area ratios of the respectivedivided areas with reference to an unrepresented table in thenumber-of-terminals estimation device 10 or from the outside.

Next, the conversion unit 24 calculates populations in the respectivedivided areas. For example, supposing that a population in area A in (a)of FIG. 18 is 800, a population in the divided area A-2 is calculated as200 (i.e., 800×25%) as shown in FIG. 20. Similarly, supposing thatpopulations in the areas B and C are 500 and 750, a population in thedivided area B-1 with the area ratio of 80% in the area B is calculatedas 400 (i.e., 500×80%) and a population in the divided area C-4 with thearea ratio of 80% in the area C is calculated as 600 (i.e., 750×80%).

Furthermore, the conversion unit 24 calculates the sum of thepopulations in the plurality of divided areas included in one mesh tocalculate a population in the mesh. In the example of FIG. 20, theconversion unit 24 calculates a total of the populations in the dividedareas A-2, B-1, and C-4 included in one mesh as 1200 (i.e., 200+400+600)and defines this 1200 as a population in the mesh.

As described above, the populations in respective totalization units canbe converted into populations in respective output units, in theenvironment in which there are communication regions of outdoor stationsusing a single frequency band.

The sixth embodiment described the processing to convert the populationsin respective totalization units into populations in respective outputunits on the basis of the fourth embodiment, but the sixth embodiment isalso applicable to the aforementioned fifth embodiment. Furthermore, theconversion process described in the sixth embodiment is also applicableto cases where the numbers of terminals in respective totalization unitsare converted into the numbers of terminals in respective output units,and application thereof to the aforementioned first to third embodimentsallows the numbers of terminals in respective totalization units to beconverted into the numbers of terminals in respective output units.

Seventh Embodiment

The seventh embodiment will describe an example in which estimatedvalues (numbers of terminals or populations) in respective areas(sectors) of totalization units are converted into estimated values inrespective output units (meshes as an example herein), in an environmentwhere there are at least two geographically overlapping communicationregions out of communication regions of indoor stations andcommunication regions of outdoor stations using respective frequencybands with different coverage areas.

The function block configuration of the number-of-terminals estimationdevice in the seventh embodiment is the same as in the sixth embodiment,except for the processing of the conversion unit 24, and therefore theprocessing of the conversion unit 24 will be described on the basis ofFIGS. 21 and 22.

In an environment where there are geographically overlappingcommunication regions of communication regions of indoor stations andcommunication regions of outdoor stations using respective frequencybands with different coverage areas (outdoor 2 GHz/1.7 GHz and outdoor800 MHz), as shown in FIG. 21, the conversion unit 24 performs theconversion process described in the sixth embodiment, for each of thecommunication regions, to obtain populations in respective output units(meshes) about the respective communication regions, and finallytotalizes the populations about the respective communication regions ineach of output units to obtain populations in respective output units.

In the example of FIG. 21, the conversion unit 24 first performs theconversion process described in the sixth embodiment, for each of thecommunication regions of the outdoor stations using outdoor 2 GHz/1.7GHz, the communication regions of the outdoor stations using outdoor 800MHz, and the communication regions of the indoor stations. For example,in the communication regions of the outdoor stations using outdoor 2GHz/1.7 GHz, supposing that a divided area as an overlap between outputunit Q and area A has an area ratio of 40% to the entire area of area A,an estimated population of 100 in area A is multiplied by the area ratioof 0.4 to obtain an estimated population of 40 for the divided area asan overlap between output unit Q and area A. Similarly, an estimatedpopulation of 3 is obtained for a divided area as an overlap betweenoutput unit Q and area B (estimated population of 30 in area B×arearatio of 0.1), and an estimated population of 5 is obtained for adivided area as an overlap between output unit Q and area C (estimatedpopulation of 100 in area C×area ratio of 0.05). Concerning thecommunication regions of the outdoor stations using outdoor 800 MHz,similarly, an estimated population of 3 is obtained for a divided areaas an overlap between output unit Q and area D (estimated population of10 in area D×area ratio of 0.3), and an estimated population of 9 isobtained for a divided area as an overlap between output unit Q and areaF (estimated population of 30 in area F×area ratio of 0.3). On the otherhand, concerning the indoor stations, the areas of the individual indoorstations as coverage areas are very small, and in the example of FIG.21, the whole of area L of one indoor station overlaps the output unitQ; therefore, the area ratio can be considered to be 100%. Therefore, anestimated population of 10 in area L is multiplied by the area ratio 1.0to obtain an estimated population of 10 for the area as an overlapbetween output unit Q and area L (the whole of area L in this example).

Finally, the conversion unit 24 totalizes the estimated populations ofthe overlapping areas between output unit Q and each of the areas, whichwere obtained as described above, to obtain an estimated population of70 in output unit Q. In the manner as described above, the populationsin the respective totalization units can be converted into the estimatedpopulation in output unit Q.

FIG. 21 shows the conversion into the estimated population in one outputunit Q, but the same process may be executed for other output units toimplement the conversion into estimated populations in all output unitsas targets.

FIG. 22 shows a matrix equation for conversion into estimatedpopulations in n output units as targets. Namely, in the right-hand sideof the equation of FIG. 22, the following components:

Pop_(b) _(j)

(where j is an integer of not less than 1 and not more than m (m is thenumber of totalization units overlapping any one of n output units astargets)) mean populations (estimated populations) in respectivetotalization units obtained by estimation; in the left-hand side, thefollowing components:

Pop_(a) _(i)

(where i is an integer of not less than 1 and not more than n) meanpopulations in respective output units; in the right-hand side of thematrix equation, the following components:

k _(b) _(j) _(→a) _(i)

mean conversion coefficients for conversion from populations intotalization units b_(j) into populations in output units a_(i). Theconversion coefficients herein correspond to the aforementioned arearatios of divided areas to the entire original area.

Each of the conversion coefficients in FIG. 22 can be preliminarilydetermined from a positional relation between estimation units (e.g.,areas) and output units (e.g., meshes); when each of the conversioncoefficients is preliminarily determined and the equation of FIG. 22 isstored, populations (estimated populations) in respective totalizationunits obtained by estimation can be readily and quickly converted intopopulations in respective output units, using the equation of FIG. 22.

In the manner as described above, the populations in respectivetotalization units can be converted into populations in respectiveoutput units, even in the environment where there are at least twogeographically overlapping communication regions out of communicationregions of indoor stations and communication regions of outdoor stationsusing respective frequency bands with different coverage areas.

The sixth and seventh embodiments described the examples in which thepopulations in the respective totalization units preliminarily obtainedby estimation were converted into populations in respective outputunits, but the following modification examples can also be contemplated.

Together with the feature amounts and scaling factors, at least onecombination of output unit IDs about respective output unitsgeographically overlapping with areas (totalization units) to whichlocation data belongs, and area ratios of overlapping portions with theoutput units (i.e., area ratios of the overlapping portions to the wholeof the totalization units) is stored in association with the locationdata in the feature amount storage unit 17A.

In this case, (feature amount×area ratio) is calculated for locationdata associated with the same output unit ID, the results of (featureamount×area ratio) are totalized for each output unit, and the number ofterminals in each output unit may be estimated by dividing the totalvalue in each output unit by (observation period length×2).

Another conceivable method is as follows: (feature amount×arearatio×scaling factor) is calculated for location data associated withthe same output unit ID; the results of (feature amount×arearatio×scaling factor) are totalized for each output unit; the totalvalue in each output unit is divided by (observation period length×2) toestimate a population in each output unit. On that occasion, populationsin respective output units and in respective population estimation unitsmay be estimated by totalizing values of (feature amount×arearatio×scaling factor) in respective output units, for each of populationestimation units (e.g., attributes, time zones, or the like), anddividing total values in respective output units and in respectivepopulation estimation units thus obtained, by (observation periodlength×2).

The populations in respective output units and in respective populationestimation units may be estimated as follows: concerning location dataassociated with the same output unit ID, (feature amount×area ratio) iscalculated for each of population estimation units (e.g., attributes,time zones, or the like); the results of (feature amount×area ratio) inthe respective population estimation units are totalized for each ofoutput units; total values in respective output units and in respectivepopulation estimation units obtained are multiplied by scaling factorsabout the population estimation units; the multiplication results thusobtained are divided by (observation period length×2) to obtain thepopulations in respective output units and in respective populationestimation units. In this case, however, it is not essential toassociate the location data with the scaling factors, and the foregoingmultiplication may be carried out using the scaling factors about therespective population estimation units, for example, those preliminarilystored separately from the location data or those acquired from theoutside. After the populations in respective output units and inrespective population estimation units are obtained by the aboveprocessing, the populations in respective output units and in respectivepopulation estimation units may be totalized for each of output units toestimate the populations in respective output units.

As described previously, it is not essential to determine the scalingfactors for the respective population estimation units, and, forexample, a common scaling factor to all may be used, without need foruse of the scaling factors for the respective population estimationunits. In this case, as an example, a population may be estimated bytotalizing the values of (feature amount×area ratio) on all pieces oflocation data as targets, multiplying the obtained total value by thecommon scaling factor, and dividing the multiplication result by(observation period length×2).

The feature amounts used in the various modification examples asdescribed above may be calculated by any one of the methods in theaforementioned first to third embodiments.

The seventh embodiment described the process to convert the populationsin respective totalization units into populations in respective outputunits, based on the fourth embodiment, but the seventh embodiment isalso applicable to the aforementioned fifth embodiment. The conversionprocess described in the seventh embodiment is also applicable to thecase where the numbers of terminals in respective totalization units areconverted into the numbers of terminals in respective output units, andwhen it is applied to the aforementioned first to third embodiments, thenumbers of terminals in respective totalization units can be convertedinto the numbers of terminals in respective output units.

Eighth Embodiment

The eighth embodiment will describe an embodiment to perform anunidentifiability securing process for removing information withindividual identifiability from the location data and attributeinformation.

As shown in FIG. 23, the function block configuration of thenumber-of-terminals estimation device 10 in the eighth embodiment is aconfiguration wherein an unidentifiability securing unit 25(unidentifiability securing means) to perform the unidentifiabilitysecuring process is added between the location data acquisition unit 11and the storage unit 12 in the function block configuration of thenumber-of-terminals estimation device in the fourth embodiment (FIG.12).

The unidentifiability securing unit 25 performs the unidentifiabilitysecuring process including conversion into irreversible code by aone-way function, on the identification information (e.g., a phonenumber) included in the location data. The one-way function is usedherein in order to prevent restoration from information after theconversion, and the one-way function to be used herein can be a keyedhash function based on the hash function recommended by national andinternational evaluation projects and evaluation organizations, forexample, as shown in FIG. 24.

When a process using the attribute information of a user of a mobileterminal is carried out, as shown in FIG. 24, the unidentifiabilitysecuring unit 25 performs the unidentifiability securing processincluding the conversion into irreversible code by the one-way function,on an individual-identifiable number (e.g., a phone number) in theattribute information, before the process. Furthermore, theunidentifiability securing unit 25 may delete name information in theattribute information, replace date-of-birth information with ageinformation, and replace address information with numbered subdivisionaddress information excluding street number information.

Since the unidentifiability securing process by the unidentifiabilitysecuring unit 25 as described above can remove the information withindividual identifiability from the location data and attributeinformation, it can prevent such trouble that an individual isidentified from the location data or the attribute information.

The eighth embodiment described the unidentifiability securing process,based on the fourth embodiment, but the eighth embodiment is alsoapplicable to the aforementioned fifth embodiment. The unidentifiabilitysecuring process described in the eighth embodiment does not always haveto be limited only to the population estimation, but can also be appliedto the number-of-terminals estimation described in the first to thirdembodiments. The unidentifiability securing process described in theeighth embodiment is also applicable to the cases to perform theconversion process as described in the sixth and seventh embodiments.

Ninth Embodiment

The ninth embodiment will describe an embodiment wherein a certainestimated value (the number of terminals, a population, or the like) isoutput after execution of a concealment process on an estimated value onthe basis of a predetermined reference.

As shown in FIG. 25, the function block configuration of thenumber-of-terminals estimation device 10 in the ninth embodiment is aconfiguration wherein a concealment process unit 26 (concealment processmeans) to perform the concealment process is added between thepopulation estimation unit 21 and the population output unit 22 in thefunction block configuration of the number-of-terminals estimationdevice in the eighth embodiment (FIG. 23).

The concealment process unit 26 performs, for example, the concealmentprocess shown in FIG. 26, when receiving an estimated value (estimatedpopulation or the like) from the population estimation unit 21. Namely,the concealment process unit 26 determines whether the number of sourceterminals indicative of from how may terminals the location data in eacharea (cell) as foundation of the estimation is acquired, is less than apredetermined reference value (10 as an example) for a determination onnecessity of the concealment process (step S61 in FIG. 26). The numberof source terminals indicates the unique number of terminals withoutredundancy of identical terminal. Execution of the determination hereinrequires the number of source terminals of the location data in eacharea (cell), and an example thereof is as follows: in totalizing thefeature amounts associated with the location data, the populationestimation unit 21 counts the number of identification information(e.g., hashed phone numbers after the aforementioned unidentifiabilitysecuring process by the unidentifiability securing unit 25) in thelocation data; the population estimation unit 21 transmits informationof the total number of hashed phone numbers thus obtained, to theconcealment process unit 26; the concealment process unit 26 uses thetotal number of hashed phone numbers as the number of source terminalsof the location data in each area (cell).

In step S61, if the number of source terminals of the location data ineach area (cell) as foundation of the estimation is less than thereference value, the concealment process unit 26 sets the estimatedvalue about the area (cell) to zero, thereby concealing the estimatedvalue (step S62). The concealment method herein is not limited tosetting the estimated value to zero, but another method may be adopted,for example, such as a method of expressing the estimated value by apredetermined letter or mark (e.g., “X” or the like).

On the other hand, when it is determined in step S61 that the number ofsource terminals of the location data in each area (cell) as foundationof the estimation is not less than the reference value, the concealmentprocess unit 26 performs randomized rounding of a class interval used inoutput of estimated value, which is described below, on the estimatedvalue about the area (cell) (step S63). Namely, let x be the estimatedvalue about an area (cell) and k be the class interval; then, whenkn≦x≦k(n+1) (n is an integer), the concealment process unit 26 roundsthe estimated value x to k(n+1) with a probability of (x−kn)/k and to knwith a probability of (k(n+1)−x)/k.

For example, in a case where the estimated value x is 23 and where theclass interval k is 10, k×2≦x<k(2+1) and thus n=2; the estimated value“23” is rounded to “30” with the probability of 0.3 (probability of 30%)and to “20” with the probability of 0.7 (probability of 70%).

The concealment process by the concealment process unit 26 as describedabove can prevent an individual from being identified from theestimation result and enhance availability of the estimation result. Itcan also prevent such trouble that the concealed value can be speculatedfrom other values.

The class interval in the randomized rounding may be determined asfollows: the location data with a maximum scaling factor is extractedfrom the location data used in totalization; the scaling factor ismultiplied by a predetermined number (e.g., 10); the result of themultiplication is set as the class interval. It is also possible topreliminarily determine the class interval and perform the randomizedrounding therewith. On that occasion, it is possible to adopt a processrule of discarding data of an area (cell) including the location datathe scaling factor of which exceeds a predetermined ratio (e.g., 1/10)of the predetermined class interval.

The concealment process by the concealment process unit 26 does not haveto be limited only to the process shown in FIG. 26, but another processmay also be adopted. For example, in step S61 in FIG. 26, adetermination may be made on whether “the number of location data ineach area (cell) as foundation of the estimation” is less than areference value or on whether “an output estimated value (population orthe number of terminals)” is less than a reference value, instead of“the number of source terminals of the location data in each area (cell)as foundation of the estimation.” When the determination in step S61 inFIG. 26 is negative, the process in step S63 may be omitted.

The ninth embodiment described the concealment process based on theeighth embodiment (the embodiment to perform the unidentifiabilitysecuring process), but it can also be applied to cases where theunidentifiability securing process as in the eighth embodiment is notcarried out. The ninth embodiment is also applicable to the populationestimation described in the fourth and fifth embodiments and is alsoapplicable to the number-of-terminals estimation described in the firstto third embodiments. The ninth embodiment is also applicable to caseswhere the conversion process described in the sixth and seventhembodiments is carried out.

Now, let us describe the population estimation units and the outputunits and output forms associated with output in the aforementionedvarious embodiments, below.

A population estimation unit can be set according to at least one ofattributes (ages, genders, addresses, or the like) of users, time zones,and places (sectors, meshes, or the like). For example, when a certainattribute is defined as a population estimation unit, the populationestimation unit 21 can estimate a population in each attribute as thepopulation estimation unit, as also described in the fourth embodiment,by multiplying the feature amounts associated with respective pieces ofobservation target location data, by the scaling factor, in eachattribute as the population estimation unit, based on the attributeinformation associated with the respective pieces of observation targetlocation data, and dividing the sum of the multiplication results by(observation length×2). For example, as shown in FIG. 16, the device canoutput the population estimated for the observation area, and thepopulations in respective attributes such as genders, age groups, andaddresses. It is also possible to adopt a combinational condition ofmultiple attributes (e.g., a condition of “women residing in Tokyo” as acombination of a gender with an address). When time zones, for example,at one-hour intervals in each day are defined as population estimationunits, the population estimation unit 21 can estimate populations inrespective time zones by specifying to which time zone each observationtarget location data corresponds, based on the acquisition timeinformation in each observation target location data, multiplying thefeature amounts associated with respective pieces of observation targetlocation data, by scaling factors, and dividing the sum of themultiplication results by (observation length×2), in each of the timezones at one-hour intervals. When the time zones are set as populationestimation units, they may be discontinuous time zones along a timesequence (e.g., time zones from 13:00 to 14:00 every day, time zonesfrom 10:00 to 11:00 in Saturday and Sunday every week, and so on.

On the other hand, the output unit associated with the output can alsobe set, in the same manner as the estimation unit, according to at leastone of attributes (ages, genders, addresses, or the like) of users, timezones, and places (sectors, meshes, or the like).

An output form associated with the output can be selected from a varietyof output forms, for example, such as a drawing showing a populationdistribution shown in (a) of FIG. 27, a drawing showing a time-seriespopulation change shown in (b) of FIG. 27, and a drawing showing apopulation composition shown in (c) of FIG. 27. Furthermore, concerningeach of these output forms, the output may be provided for each one orcombination of two or more out of the user attributes (ages, genders,addresses, or the like), time zones, and places (sectors, meshes, or thelike), and the output can be, for example, a population composition ofmen and women in respective age groups at 5-year intervals as shown in(c) of FIG. 27.

Next, a modification example about the feature amount will be described.The aforementioned first and second embodiments showed the examples inwhich the time difference between the preceding and following locationdata (the time difference between the second location data and the thirdlocation data) before and after the location data as a target forcalculation of the feature amount (first location data) was calculatedas the feature amount of the first location data. Expressing this by anequation, the feature amount can be expressed by Equation (7) below. Thebelow Equation (7) is a modification of the aforementioned Equation (4)and is equivalent to Equation (4) (namely, there is no change in theconception of Equation (4)).

w _(ij) =u _(i(j+1)) −u _(i(j−1))  (7)

The present modification example shows another variation of the featureamount calculation method in the feature amount calculation unit 17.

In the present modification example, when the feature amount calculationunit 17 calculates the feature amount of the first location data, ittakes account of class information (e.g., below-described generationfactor (generation timing) of location data) on the second location dataand the third location data. Specifically, the feature amountcalculation unit 17 calculates a value of a multiplication of the timedifference between the third location data and the first location databy a correction factor α corresponding to the class information of thethird location data (generation factor herein) and calculates a value ofa multiplication of the time difference between the first location dataand the second location data by a correction factor β corresponding tothe class information of the second location data (generation factorherein). However, instead of the above factors, the feature amountcalculation unit 17 may determine the correction factor α or β accordingto the class information of the first location data or may determine thecorrection factor β according to the class information of the first andsecond location data and determine the correction factor α according tothe class information of the first and third location data. Then thefeature amount calculation unit 17 defines a value obtained by addingthe results of these multiplications, as the feature amount of the firstlocation data. When the feature amount calculation process in thefeature amount calculation unit 17 is expressed by an equation, it isrepresented by Equation (8) below.

w _(ij)=α(u _(i(j+1)) −u _(ij))+β(u _(ij) −u _(i(j−1))  (8)

For example, when the location data is the location registrationinformation, the class information about the second location data andthe third location data can be information about the generation factorof the location registration information, and this information about thegeneration factor is included in the generated location registrationinformation. Examples of such generation factors of locationregistration information include a crossing of a terminal across aborder of a location registration area (Location Area), generation basedon location registration performed at regular intervals, execution of anattaching process by a power-on operation of a terminal or the like,execution of a detaching process by a power-off operation of a terminal,and so on, and set values of the correction factors α and β arepreliminarily defined corresponding to these generation factors. Thenthe feature amount calculation unit 17 can set the correction factor αon the third location data in accordance with the information about thegeneration factor of the third location data and set the correctionfactor β on the second location data in accordance with the informationabout the generation factor of the second location data. The correctionfactors α, β both may be preliminarily determined as values of not lessthan 0 and not more than 1. However, this numerical range is notessential.

For example, in the case of the location registration information thegeneration timing of which is irrespective of the location of theterminal like the location registration information based on locationregistrations performed at regular intervals, expectations of time whenthe terminal has stayed in a current sector are considered to be thesame before and after generation of the location registrationinformation. On the other hand, when the location registrationinformation is one generated because of a crossing of a terminal acrossa location registration area border, it can be determined that theterminal did not stay in the current sector, at least before generationof the location registration information. For this reason, a duration ofperiod when the terminal stayed in the current sector before generationof the location registration information can be considered to be 0, andwhen the class information (generation factor) of the first locationdata is “a crossing across a location registration area border,” thecorrection factor β in above Equation (8) (i.e., the correction factor βabout the time difference from the immediately-preceding location data)can be set to 0. This allows the device to calculate the feature amountbetter agreeing with the actual condition. When the class information(generation factor) of the first location data is “a crossing across alocation registration area border” in this manner, the calculation ofthe feature amount with the correction factor β of 0 can achieve thesame effect as in the aforementioned third embodiment.

As described above, when the feature amount calculation unit 17calculates the feature amount of the target location data (firstlocation data), it corrects the time differences from the secondlocation data and the third location data in accordance with the classinformation on the second and third location data (generation factor ofthe location data as an example) being the preceding and flowinglocation data before and after the first location data, and calculatesthe feature amount using the corrected time difference. This allows thedevice to calculate the feature amount more accurately, based on theclass information of the location data.

LIST OF REFERENCE SIGNS

1: communication system; 10: number-of-terminals estimation device; 11:location data acquisition unit; 12: storage unit; 13: observation periodacquisition unit; 14: observation area acquisition unit; 15: observationtarget acquisition unit; 16: preceding and following location dataacquisition unit; 17: feature amount calculation unit; 17A: featureamount storage unit; 18: number-of-terminals estimation unit; 19:number-of-terminals output unit; 21: population estimation unit; 22:population output unit; 23: attribute and scaling factor storage unit;24: conversion unit; 25: unidentifiability securing unit; 26:concealment process unit; 100: mobile terminal; 200: BTS; 300: RNC; 400:exchange; 500: management center; 501: social sensor unit; 502:peta-mining unit; 503: mobile demography unit; 504: visualizationsolution unit; 700: various processing node.

1. A number-of-terminals estimation device comprising: location dataacquisition means for acquiring location data including identificationinformation to identify a mobile terminal, location information about alocation of the mobile terminal, and location acquisition timeinformation on a time when the location information is acquired;preceding and following location data acquisition means for, concerninga piece of first location data, acquiring location acquisition timeinformation of second location data which is location data immediatelypreceding the first location data, and location acquisition timeinformation of third location data which is location data immediatelyfollowing the first location data, from location data including the sameidentification information as that of the first location data; featureamount calculation means for calculating a feature amount of the firstlocation data, based on at least two of the location acquisition timeinformation of the first location data, the location acquisition timeinformation of the second location data, and the location acquisitiontime information of the third location data; observation targetacquisition means for acquiring as observation target location data, oneor more pieces of location data including location acquisition timeinformation after an observation start time and before an observationend time about an observation period to be observed, and includinglocation information associated with observation area information aboutan observation area to be observed; and number-of-terminals estimationmeans for estimating the number of terminals located in the observationarea during the observation period, based on a feature amount of theobservation target location data, and a length of the observation periodwhich is a difference between the observation start time and theobservation end time.
 2. The number-of-terminals estimation deviceaccording to claim 1, wherein the feature amount calculation meanscalculates a difference between a location acquisition time of thesecond location data and a location acquisition time of the thirdlocation data, as the feature amount of the first location data, andwherein the number-of-terminals estimation means estimates the number ofterminals to be a numeral obtained by dividing a sum of feature amountsof the observation target location data by twice the length of theobservation period.
 3. The number-of-terminals estimation deviceaccording to claim 2, wherein the feature amount calculation meansoperates as follows: when a difference between a location acquisitiontime of the first location data and the location acquisition time of thesecond location data is larger than a predetermined value, the featureamount calculation means calculates the feature amount of the firstlocation information, using as the location acquisition time of thesecond location data, a time set backward by a predetermined time fromthe location acquisition time of the first location data; when adifference between the location acquisition time of the first locationdata and the location acquisition time of the third location data islarger than a predetermined value, the feature amount calculation meanscalculates the feature amount of the first location information, usingas the location acquisition time of the third location data, a time setforward by a predetermined time from the location acquisition time ofthe first location data.
 4. The number-of-terminals estimation deviceaccording to claim 1, wherein the feature amount calculation meansoperates as follows: the feature amount calculation means makes adetermination on whether or not the first location data includeslocation registration information generated due to a crossing across alocation registration area border, and a determination on whether or notthe third location data includes location registration informationgenerated due to a crossing across a location registration area border;the feature amount calculation means calculates the feature amount ofthe first location data, using at least two of the location acquisitiontime information of the first location data, the location acquisitiontime information of the second location data, and the locationacquisition time information of the third location data, according tothe result of the determination on whether or not the first locationdata includes location registration information generated due to acrossing across a location registration area border and the result ofthe determination on whether or not the third location data includeslocation registration information generated due to a crossing across alocation registration area border.
 5. The number-of-terminals estimationdevice according to claim 4, wherein the feature amount calculationmeans operates as follows: when the first location data includeslocation registration information generated due to a crossing across alocation registration area border, the feature amount calculation meanssets a location acquisition time of the first location data to a firstvariable; when the first location data does not include locationregistration information generated due to a crossing across a locationregistration area border, the feature amount calculation means sets amidpoint time between the location acquisition time of the firstlocation data and a location acquisition time of the second locationdata to the first variable; when the third location data includeslocation registration information generated due to a crossing across alocation registration area border, the feature amount calculation meanssets a location acquisition time of the third location data to a secondvariable; when the third location data does not include locationregistration information generated due to a crossing across a locationregistration area border, the feature amount calculation means sets amidpoint time between the location acquisition time of the firstlocation data and the location acquisition time of the third locationdata to the second variable; the feature amount calculation meanscalculates the feature amount of the first location data, based on adifference between the set first variable and second variable.
 6. Thenumber-of-terminals estimation device according to claim 4, wherein thefeature amount calculation means operates as follows: when a differencebetween a location acquisition time of the first location data and afirst variable is larger than a predetermined value, the feature amountcalculation means calculates the feature amount of the first locationdata, using as the first variable, a time set backward by apredetermined time from the location acquisition time of the firstlocation data; when a difference between the location acquisition timeof the first location data and a second variable is larger than apredetermined value, the feature amount calculation means calculates thefeature amount of the first location data, using as the second variable,a time set forward by a predetermined time from the location acquisitiontime of the first location data.
 7. The number-of-terminals estimationdevice according to claim 1, wherein the preceding and followinglocation data acquisition means defines each piece of the observationtarget location data acquired by the observation target acquisitionmeans, as the first location data, and acquires, for the first locationdata, the location acquisition time information of the second locationdata and the location acquisition time information of the third locationdata, wherein the feature amount calculation means calculates thefeature amount of each piece of the observation target location data,and wherein the number-of-terminals estimation means estimates thenumber of terminals, using feature amounts of respective pieces of theobservation target location data obtained by calculation.
 8. Thenumber-of-terminals estimation device according to claim 1, wherein thepreceding and following location data acquisition means defines eachpiece of all location data acquired by the location data acquisitionmeans, as the first location data, and acquires, for the first locationdata, the location acquisition time information of the second locationdata and the location acquisition time information of the third locationdata, wherein the feature amount calculation means calculates thefeature amount of each piece of all the location data, and wherein thenumber-of-terminals estimation means estimates the number of terminals,using feature amounts of the observation target location data amongfeature amounts of respective pieces of all the location data obtainedby calculation.
 9. A number-of-terminals estimation device comprising:location data acquisition means for acquiring location data includingidentification information to identify a mobile terminal, locationinformation about a location of the mobile terminal, and locationacquisition time information on a time when the location information isacquired; preceding and following location data acquisition means for,concerning a piece of first location data, acquiring locationacquisition time information of second location data which is locationdata immediately preceding the first location data, and locationacquisition time information of third location data which is locationdata immediately following the first location data, from location dataincluding the same identification information as that of the firstlocation data; feature amount calculation means for calculating afeature amount of the first location data, based on at least a locationacquisition time of the second location data and a location acquisitiontime of the third location data; observation target acquisition meansfor acquiring as observation target location data, one or more pieces oflocation data including location acquisition time information after anobservation start time and before an observation end time about anobservation period to be observed, and including location informationassociated with observation area information about an observation areato be observed; and number-of-terminals estimation means for estimatingthe number of terminals located in the observation area during theobservation period, based on a feature amount of the observation targetlocation data, and a length of the observation period which is adifference between the observation start time and the observation endtime.
 10. The number-of-terminals estimation device according to claim1, further comprising scaling factor storage means for storing anscaling factor for conversion of the number of terminals into apopulation, wherein the number-of-terminals estimation means estimatesat least one of a population in the observation area during theobservation period, and populations in respective population estimationunits which are units of estimation for population, based on featureamounts of the observation target location data, the length of theobservation period, and the scaling factor.
 11. The number-of-terminalsestimation device according to claim 10, wherein the scaling factor isderived using the number of terminals estimated based on the featureamounts and the length of the observation period.
 12. Thenumber-of-terminals estimation device according to claim 1, furthercomprising: conversion means for converting estimated values inrespective observation areas obtained by estimation by thenumber-of-terminals estimation means, into estimated values inrespective output units different from the observation areas, based onarea ratios of overlap regions between the output units and theobservation areas to the observation areas.
 13. The number-of-terminalsestimation device according to claim 12, wherein the conversion meansoperates as follows: when there are at least two communication areas outof a communication area of an indoor station and communications areas ofa plurality of outdoor stations using respective frequency bands withdifferent coverage areas, overlapping in a geographically identicalobservation area, the conversion means performs conversion into theestimated values in the respective output units based on the area ratiosfor each of the overlapping communication regions and addition of theestimated values after the conversion for each of the communicationregions, thereby obtaining the estimated values in the respective outputunits.
 14. The number-of-terminals estimation device according to claim1, wherein the number-of-terminals estimation means estimatespopulations separately in respective output units and in respectivepopulation estimation units, based on feature amounts of the observationtarget location data, the length of the observation period, an scalingfactor for conversion of the number of terminals into a population, andarea ratios of overlap regions between observation areas and outputunits different from the observation areas to the observation areas. 15.The number-of-terminals estimation device according to claim 14,wherein, prior to the estimation of populations by thenumber-of-terminals estimation means, location data is associated withthe feature amount, the scaling factor, and a combination of the arearatio and an output unit ID related to the area ratio, and wherein thenumber-of-terminals estimation means calculates (feature amount×arearatio×scaling factor) on location data with which the same output unitID is associated, totalizes values of (feature amount×area ratio×scalingfactor) in respective output units obtained, for each of the populationestimation units, and estimates the populations in respective outputunits and in respective population estimation units, based on totalvalues in respective output units and in respective populationestimation units obtained and the length of the observation period. 16.The number-of-terminals estimation device according to claim 1, furthercomprising: observation period acquisition means for acquiringobservation period information including a set of an observation starttime and an observation end time; and observation area acquisition meansfor acquiring observation area information associated with one or morepieces of location information.
 17. The number-of-terminals estimationdevice according to claim 1, further comprising: output means foroutputting the estimated value obtained.
 18. The number-of-terminalsestimation device according to claim 17, wherein an output form by theoutput means is allowed to be at least one of a drawing showing apopulation distribution, a drawing showing a time-series populationchange, and a drawing showing a population composition, and wherein anoutput unit by the output means is allowed to be set according to atleast one of an attribute of a user of a mobile terminal, a time zone,and a place.
 19. The number-of-terminals estimation device according toclaim 1, further comprising: unidentifiability securing means forperforming an unidentifiability securing process including a conversioninto irreversible code by a one-way function on identificationinformation included in the location data acquired by the location dataacquisition means, wherein, when a process using attribute informationof a user of a mobile terminal is carried out, the unidentifiabilitysecuring means performs the unidentifiability securing process on theattribute information, before the process.
 20. The number-of-terminalsestimation device according to claim 1, further comprising: concealmentprocess means for, before an estimated value obtained is output,performing a concealment process on the estimated value on the basis ofa predetermined reference.
 21. The number-of-terminals estimation deviceaccording to claim 20, wherein the concealment process means determineswhether or not the number of source terminals indicative of from howmany terminals the location data in each area as foundation ofestimation was acquired, is less than a reference value for adetermination that the concealment process is needed, and wherein whenthe number of source terminals of the location data in an area is lessthan the reference value, the concealment process means conceals theestimated value about the area.
 22. The number-of-terminals estimationdevice according to claim 21, wherein the concealment process meansoperates as follows: when the number of source terminals of the locationdata in an area is not less than the reference value, the concealmentprocess means rounds the estimated value on the area, based on an upperlimit value and a lower limit value of a class to which the estimatedvalue on the area belongs out of a plurality of classes used in outputof estimated value, a class interval, and the estimated value, to theupper limit value and the lower limit value with respective probabilityvalues according to a difference from the upper limit value and adifference from the lower limit value.
 23. A number-of-terminalsestimation method executed by a number-of-terminals estimation device,comprising: a location data acquisition step of acquiring location dataincluding identification information to identify a mobile terminal,location information about a location of the mobile terminal, andlocation acquisition time information on a time when the locationinformation is acquired; an observation target acquisition step ofacquiring as observation target location data, one or more pieces oflocation data including location acquisition time information after anobservation start time and before an observation end time about anobservation period to be observed, and including location informationassociated with observation area information about an observation areato be observed; a preceding and following location data acquisition stepof defining each piece of the observation target location data acquired,as first location data, and, concerning each piece of the first locationdata, acquiring location acquisition time information of second locationdata which is location data immediately preceding the first locationdata and location acquisition time information of third location datawhich is location data immediately following the first location data,from location data including the same identification information as thatof the first location data; a feature amount calculation step ofcalculating a feature amount of each piece of the observation targetlocation data, based on at least two of the location acquisition timeinformation of the first location data, the location acquisition timeinformation of the second location data, and the location acquisitiontime information of the third location data; and a number-of-terminalsestimation step of estimating the number of terminals located in theobservation area during the observation period, based on the featureamount of the observation target location data obtained by calculation,and a length of the observation period which is a difference between theobservation start time and the observation end time.
 24. Anumber-of-terminals estimation method executed by a number-of-terminalsestimation device, comprising: a location data acquisition step ofacquiring location data including identification information to identifya mobile terminal, location information about a location of the mobileterminal, and location acquisition time information on a time when thelocation information is acquired; a preceding and following locationdata acquisition step of defining each piece of all the location dataacquired, as first location data, and, concerning each piece of thefirst location data, acquiring location acquisition time information ofsecond location data which is location data immediately preceding thefirst location data and location acquisition time information of thirdlocation data which is location data immediately following the firstlocation data, from location data including the same identificationinformation as that of the first location data; a feature amountcalculation step of calculating feature amounts of respective pieces ofall the location data, based on at least two of the location acquisitiontime information of the first location data, the location acquisitiontime information of the second location data, and the locationacquisition time information of the third location data; an observationtarget acquisition step of acquiring as observation target locationdata, one or more pieces of location data including location acquisitiontime information after an observation start time and before anobservation end time about an observation period to be observed, andincluding location information associated with observation areainformation about an observation area to be observed; and anumber-of-terminals estimation step of estimating the number ofterminals located in the observation area during the observation period,based on a feature amount of the observation target location data out ofthe feature amounts of the respective pieces of all the locationinformation obtained by calculation, and a length of the observationperiod which is a difference between the observation start time and theobservation end time.
 25. A number-of-terminals estimation methodexecuted by a number-of-terminals estimation device, comprising: alocation data acquisition step of acquiring location data includingidentification information to identify a mobile terminal, locationinformation about a location of the mobile terminal, and locationacquisition time information on a time when the location information isacquired; an observation target acquisition step of acquiring asobservation target location data, one or more pieces of location dataincluding location acquisition time information after an observationstart time and before an observation end time about an observationperiod to be observed, and including location information associatedwith observation area information about an observation area to beobserved; a preceding and following location data acquisition step ofdefining each piece of the observation target location data acquired, asfirst location data, and, concerning each piece of the first locationdata, acquiring location acquisition time information of second locationdata which is location data immediately preceding the first locationdata and location acquisition time information of third location datawhich is location data immediately following the first location data,from location data including the same identification information as thatof the first location data; a feature amount calculation step ofcalculating a feature amount of each piece of the observation targetlocation data, based on at least a location acquisition time of thesecond location data and a location acquisition time of the thirdlocation data; and a number-of-terminals estimation step of estimatingthe number of terminals located in the observation area during theobservation period, based on the feature amount of the observationtarget location data obtained by calculation, and a length of theobservation period which is a difference between the observation starttime and the observation end time.
 26. A number-of-terminals estimationmethod executed by a number-of-terminals estimation device, comprising:a location data acquisition step of acquiring location data includingidentification information to identify a mobile terminal, locationinformation about a location of the mobile terminal, and locationacquisition time information on a time when the location information isacquired; a preceding and following location data acquisition step ofdefining each piece of all the location data acquired, as first locationdata, and, concerning each piece of the first location data, acquiringlocation acquisition time information of second location data which islocation data immediately preceding the first location data and locationacquisition time information of third location data which is locationdata immediately following the first location data, from location dataincluding the same identification information as that of the firstlocation data; a feature amount calculation step of calculating afeature amount of each piece of all the location data, based on at leasta location acquisition time of the second location data and a locationacquisition time of the third location data; an observation targetacquisition step of acquiring as observation target location data, oneor more pieces of location data including location acquisition timeinformation after an observation start time and before an observationend time about an observation period to be observed, and includinglocation information associated with observation area information aboutan observation area to be observed; and a number-of-terminals estimationstep of estimating the number of terminals located in the observationarea during the observation period, based on a feature amount of theobservation target location data out of the feature amounts of therespective pieces of all the location information obtained bycalculation, and a length of the observation period which is adifference between the observation start time and the observation endtime.